AI/ML Archives - 2nd Watch

Value-Focused Due Diligence with Data Analytics

Private equity funds are shifting away from asset due diligence toward value-focused due diligence. Historically, the due diligence (DD) process centered around an audit of a portfolio company’s assets. Now, private equity (PE) firms are adopting value-focused DD strategies that are more comprehensive in scope and focus on revealing the potential of an asset.

Data analytics are key in support of private equity groups conducting value-focused due diligence. Investors realize the power of data analytics technologies to accelerate deal throughput, reduce portfolio risk, and streamline the whole process. Data and analytics are essential enablers for any kind of value creation, and with them, PE firms can precisely quantify the opportunities and risks of an asset.

The Importance of Taking a Value-Focused Approach to Due Diligence

Due diligence is an integral phase in the merger and acquisition (M&A) lifecycle. It is the critical stage that grants prospective investors a view of everything happening under the hood of the target business. What is discovered during DD will ultimately impact the deal negotiation phase and inform how the sale and purchase agreement is drafted.

The traditional due diligence approach inspects the state of assets, and it is comparable to a home inspection before the house is sold. There is a checklist to tick off: someone evaluates the plumbing, another looks at the foundation, and another person checks out the electrical. In this analogy, the portfolio company is the house, and the inspectors are the DD team.

Asset-focused due diligence has long been the preferred method because it simply has worked. However, we are now contending with an ever-changing, unpredictable economic climate. As a result, investors and funds are forced to embrace a DD strategy that adapts to the changing macroeconomic environment.

With value-focused DD, partners at PE firms are not only using the time to discover cracks in the foundation, but they are also using it as an opportunity to identify and quantify huge opportunities that can be realized during the ownership period. Returning to the house analogy: during DD, partners can find the leaky plumbing and also scope out the investment opportunities (and costs) of converting the property into a short-term rental.

The shift from traditional asset due diligence to value-focused due diligence largely comes from external pressures, like an uncertain macroeconomic environment and stiffening competition. These challenges place PE firms in a race to find ways to maximize their upside to execute their ideal investment thesis. The more opportunities a PE firm can identify, the more competitive it can be for assets and the more aggressive it can be in its bids.

Value-Focused Due Diligence Requires Data and Analytics

As private equity firms increasingly adopt value-focused due diligence, they are crafting a more complete picture using data they are collecting from technology partners, financial and operational teams, and more. Data is the only way partners and investors can quantify and back their value-creation plans.

During the DD process, there will be mountains of data to sift through. Partners at PE firms must analyze it, discover insights, and draw conclusions from it. From there, they can execute specific value-creation strategies that are tracked with real operating metrics, rooted in technological realities, and modeled accurately to the profit and loss statements.

This makes data analytics an important and powerful tool during the due diligence process. Data analytics can come in different forms:

Data Scientists: PE firms can hire data science specialists to work with the DD team. Data specialists can process and present data in a digestible format for the DD team to extract key insights while remaining focused on key deal responsibilities.
Data Models: PE firms can use a robustly built data model to create a single source of truth. The data model can combine a variety of key data sources into one central hub. This enables the DD team to easily access the information they need for analysis directly from the data model.
Data Visuals: Data visualization can aid DD members in creating more succinct and powerful reports that highlight key deal issues.
Document AI: Harnessing the power of document AI, DD teams can glean insights from a portfolio company’s unstructured data to create an ever more well-rounded picture of a potential acquisition.

Data Analytics Technology Powers Value

Value-focused due diligence requires digital transformation. Digital technology is the primary differentiating factor that can streamline operations and power performance during the due diligence stage. Moreover, the right technology can increase or decrease the value of a company.

Data analytics ultimately allows PE partners to find operationally relevant data and KPIs needed to determine the value of a portfolio company. There will be enormous amounts of data for teams to wade through as they embark on the DD process. However, savvy investors only need the right pieces of information to accomplish their investment thesis and achieve value creation. Investing in robust data infrastructure and technologies is necessary to implement the automated analytics needed to more easily discover value, risk, and opportunities. Data and analytics solutions include:

Financial Analytics: Financial dashboards can provide a holistic view of portfolio companies. DD members can access on-demand insights into key areas, like operating expenses, cash flow, sales pipeline, and more.
Operational Metrics: Operational data analytics can highlight opportunities and issues across all departments.
Executive Dashboards: Leaders can access the data they need in one place. This dashboard is highly tailored to present hyper-relevant information to executives involved with the deal.

Conducting value-focused due diligence requires timely and accurate financial and operating information available on demand. 2nd Watch partners with private equity firms to develop and execute the data, analytics, and data science solutions PE firms need to drive these results in their portfolio companies. Schedule a no-cost, no-obligation private equity whiteboarding session with one of our private equity analytics consultants.

Data & AI Predictions in 2023

As we reveal our data and AI predictions for 2023, join us at 2nd Watch to stay ahead of the curve and propel your business towards innovation and success. How do we know that artificial intelligence (AI) and large language models (LLMs) have reached a tipping point? It was the hot topic at most families’ dinner tables during the 2022 holiday break.

AI has become mainstream and accessible. Most notably, OpenAI’s ChatGPT took the internet by storm, so much so that even our parents (and grandparents!) are talking about it. Since AI is here to stay beyond the Christmas Eve dinner discussion, we put together a list of 2023 predictions we expect to see regarding AI and data.

#1. Proactively handling data privacy regulations will become a top priority.

Regulatory changes can have a significant impact on how organizations handle data privacy: businesses must adapt to new policies to ensure their data is secure. Modifications to regulatory policies require governance and compliance teams to understand data within their company and the ways in which it is being accessed.

To stay ahead of regulatory changes, organizations will need to prioritize their data governance strategies. This will mitigate the risks surrounding data privacy and potential regulations. As a part of their data governance strategy, data privacy and compliance teams must increase their usage of privacy, security, and compliance analytics to proactively understand how data is being accessed within the company and how it’s being classified.

#2. AI and LLMs will require organizations to consider their AI strategy.

The rise of AI and LLM technologies will require businesses to adopt a broad AI strategy. AI and LLMs will open opportunities in automation, efficiency, and knowledge distillation. But, as the saying goes, “With great power comes great responsibility.”

There is disruption and risk that comes with implementing AI and LLMs, and organizations must respond with a people- and process-oriented AI strategy. As more AI tools and start-ups crop up, companies should consider how to thoughtfully approach the disruptions that will be felt in almost every industry. Rather than being reactive to new and foreign territory, businesses should aim to educate, create guidelines, and identify ways to leverage the technology.

Moreover, without a well-thought-out AI roadmap, enterprises will find themselves technologically plateauing, teams unable to adapt to a new landscape, and lacking a return on investment: they won’t be able to scale or support the initiatives that they put in place. Poor road mapping will lead to siloed and fragmented projects that don’t contribute to a cohesive AI ecosystem.

#3. AI technologies, like Document AI (or information extraction), will be crucial to tap into unstructured data.

According to IDC, 80% of the world’s data will be unstructured by 2025, and 90% of this unstructured data is never analyzed. Integrating unstructured and structured data opens up new use cases for organizational insights and knowledge mining.

Massive amounts of unstructured data – such as Word and PDF documents – have historically been a largely untapped data source for data warehouses and downstream analytics. New deep learning technologies, like Document AI, have addressed this issue and are more widely accessible. Document AI can extract previously unused data from PDF and Word documents, ranging from insurance policies to legal contracts to clinical research to financial statements. Additionally, vision and audio AI unlocks real-time video transcription insights and search, image classification, and call center insights.

Organizations can unlock brand-new use cases by integrating with existing data warehouses. Finetuning these models on domain data enables general-purpose models across a wide variety of use cases.

#4. “Data is the new oil.” Data will become the fuel for turning general-purpose AI models into domain-specific, task-specific engines for automation, information extraction, and information generation.

Snorkel AI coined the term “data-centric AI,” which is an accurate paradigm to describe our current AI lifecycle. The last time AI received this much hype, the focus was on building new models. Now, very few businesses need to develop novel models and algorithms. What will set their AI technologies apart is the data strategy.

Data-centric AI enables us to leverage existing models that have already been calibrated to an organization’s data. Applying an enterprise’s data to this new paradigm will accelerate a company’s time to market, especially those who have modernized their data and analytics platforms and data warehouses.

#5. The popularity of data-driven apps will increase.

Snowflake recently acquired Streamlit, which makes application development more accessible to data engineers. Additionally, Snowflake introduced Unistore and hybrid tables (OLTP) to allow data science and app teams to work together and jointly off of a single source of truth in Snowflake, eliminating silos and data replication.

Snowflake’s big moves demonstrate that companies are looking to fill gaps that traditional business intelligence (BI) tools leave behind. With tools like Streamlit, teams can harness tools to automate data sharing and deployment, which is traditionally manual and Excel-driven. Most importantly, Streamlit can become the conduit that allows business users to work directly with the AI-native and data-driven applications across the enterprise.

#6. AI-native and cloud-native applications will win.

Customers will start expecting AI capabilities to be embedded into cloud-native applications. Harnessing domain-specific data, companies should prioritize building upon module data-driven application blocks with AI and machine learning. AI-native applications will win over AI-retrofitted applications.

When applications are custom-built for AI, analytics, and data, they are more accessible to data and AI teams, enabling business users to interact with models and data warehouses in a new way. Teams can begin classifying and labeling data in a centralized, data-driven way, rather than manually and often-repeated in Excel, and can feed into a human-in-the-loop system for review and to improve the overall accuracy and quality of models. Traditional BI tools like dashboards, on the other hand, often limit business users to consume and view data in a “what happened?” manner, rather than in a more interactive, often more targeted manner.

#7. There will be technology disruption and market consolidation.

The AI race has begun. Microsoft’s strategic partnership with OpenAI and integration into “everything,” Google’s introduction of Bard and funding into foundational model startup Anthropic, AWS with their own native models and partnership with Stability AI, and new AI-related startups are just a few of the major signals that the market is changing. The emerging AI technologies are driving market consolidation: smaller companies are being acquired by incumbent companies to take advantage of the developing technologies.

Mergers and acquisitions are key growth drivers, with larger enterprises leveraging their existing resources to acquire smaller, nimbler players to expand their reach in the market. This emphasizes the importance of data, AI, and application strategy. Organizations must stay agile and quickly consolidate data across new portfolios of companies.

Conclusion

The AI ball is rolling. At this point, you’ve probably dabbled with AI or engaged in high-level conversations about its implications. The next step in the AI adoption process is to actually integrate AI into your work and understand the changes (and challenges) it will bring. We hope that our data and AI predictions for 2023 prime you for the ways it can have an impact on your processes and people.

Think you’re ready to get started? Find out with 2nd Watch’s data science readiness assessment.

Modern Data Warehouses and Machine Learning: A Powerful Pair

Artificial intelligence (AI) technologies like machine learning (ML) have changed how we handle and process data. However, AI adoption isn’t simple. Most companies utilize AI only for the tiniest fraction of their data because scaling AI is challenging. Typically, enterprises cannot harness the power of predictive analytics because they don’t have a fully mature data strategy.

To scale AI and ML, companies must have a robust information architecture that executes a company-wide data and predictive analytics strategy. This requires businesses to focus their data application beyond cost reduction and operations, for example. Fully embracing AI will require enterprises to make judgment calls and face challenges in assembling a modern information architecture that readies company data for predictive analytics.

A modern data warehouse is the catalyst for AI adoption and can accelerate a company’s data maturity journey. It’s a vital component of a unified data and AI platform: it collects and analyzes data to prepare the data for later stages in the AI lifecycle. Utilizing your modern data warehouse will propel your business past conventional data management problems and enable your business to transform digitally with AI innovations.

What is a modern data warehouse?

On-premise or legacy data warehouses are not sufficient for a competitive business. Today’s market demands organizations to rely on massive amounts of data to best serve customers, optimize business operations, and increase their bottom lines. On-premise data warehouses are not designed to handle this volume, velocity, and variety of data and analytics.

If you want to remain competitive in the current landscape, your business must have a modern data warehouse built on the cloud. A modern data warehouse automates data ingestion and analysis, which closes the loop that connects data, insight, and analysis. It can run complex queries to be shared with AI technologies, supporting seamless ML and better predictive analytics. As a result, organizations can make smarter decisions because the modern data warehouse captures and makes sense of organizational data to deliver actionable insights company-wide.

How does a modern data warehouse work with machine learning?

A modern data warehouse operates at different levels to collect, organize, and analyze data to be utilized for artificial intelligence and machine learning. These are the key characteristics of a modern data warehouse:

Multi-Model Data Storage

Data is stored in the warehouse to optimize performance and integration for specific business data.

Data Virtualization

Data that is not stored in the data warehouse is accessed and analyzed at the source, which reduces complexity, risk of error, cost, and time in data analysis.

Mixed Workloads

This is a key feature of a modern data warehouse: mixed workloads support real-time warehousing. Modern data warehouses can concurrently and continuously ingest data and run analytic workloads.

Hybrid Cloud Deployment

Enterprises choose hybrid cloud infrastructure to move workloads seamlessly between private and public clouds for optimal compliance, security, performance, and costs.

A modern data warehouse can collect and process the data to make the data easily shareable with other predictive analytics and ML tools. Moreover, these modern data warehouses offer built-in ML integrations, making it seamless to build, train, and deploy ML models.

What are the benefits of using machine learning in my modern data warehouse?

Modern data warehouses employ machine learning to adjust and adapt to new patterns quickly. This empowers data scientists and analysts to receive actionable insights and real-time information, so they can make data-driven decisions and improve business models throughout the company.

Let’s look at how this applies to the age-old question, “how do I get more customers?” We’ll discuss two different approaches to answering this common business question.

The first methodology is the traditional approach: develop a marketing strategy that appeals to a specific audience segment. Your business can determine the segment to target based on your customers’ buying intentions and your company’s strength in providing value. Coming to this conclusion requires asking inductive questions about the data:

What is the demand curve?
What product does our segment prefer?
When do prospective customers buy our product?
Where should we advertise to connect with our target audience?

There is no shortage of business intelligence tools and services designed to help your company answer these questions. This includes ad hoc querying, dashboards, and reporting tools.

The second approach utilizes machine learning within your data warehouse. With ML, you can harness your existing modern data warehouse to discover the inputs that impact your KPIs most. You simply have to feed information about your existing customers into a statistical model, then the algorithms will profile the characteristics that define an ideal customer. We can ask questions around specific inputs:

How do we advertise to women with annual income between $100,000 and $200,000 who like to ski?
What are the indicators of churn in our self-service customer base?
What are frequently seen characteristics that will create a market segmentation?

ML builds models within your data warehouse to enable you to discover your ideal customer via your inputs. For example, you can describe your target customer to the computing model, and it will find potential customers that fall under that segment. Or, you can feed the computer data on your existing customers and have the machine learn the most important characteristics.

Conclusion

A modern data warehouse is essential for ingesting and analyzing data in our data-heavy world. AI and predictive analytics feed off more data to work effectively, making your modern data warehouse the ideal environment for the algorithms to run and enabling your enterprise to make intelligent decisions. Data science technologies like artificial intelligence and machine learning take it one step further and allow you to leverage the data to make smarter enterprise-wide decisions.

2nd Watch offers a Data Science Readiness Assessment to provide you with a clear vision of how data science will make the greatest impact on your business. Our assessment will get you started on your data science journey, harnessing solutions such as advanced analytics, ML, and AI. We’ll review your goals, review your current state, and design preliminary models to discover how data science will provide the most value to your enterprise.

-Ryan Lewis | Managing Consultant at 2nd Watch

Get started with your Data Science Readiness Assessment today to see how you can stay competitive by automating processes, improving operational efficiency, and uncovering ROI-producing insights.

Here’s Why Your Data Science Project Failed (and How to Succeed Next Time)

87% of data science projects never make it beyond the initial vision into any stage of production. Even some that pass-through discovery, deployment, implementation, and general adoption fail to yield the intended outcomes. After investing all that time and money into a data science project, it’s not uncommon to feel a little crushed when you realize the windfall results you expected are not coming.

Yet even though there are hurdles to implementing data science projects, the ROI is unparalleled – when it’s done right.

Looking to get started with ML, AI, or other data science initiatives? Learn how to get started with our Data Science Readiness Assessment.

Opportunities

You can enhance your targeted marketing.

Coca-Cola has used data from social media to identify its products or competitors’ products in images, increasing the depth of consumer demographics and hyper-targeting them with well-timed ads.

You can accelerate your production timelines.

GE has used artificial intelligence to cut product design times in half. Data scientists have trained algorithms to evaluate millions of design variations, narrowing down potential options within 15 minutes.

With all of that potential, don’t let your first failed attempt turn you off to the entire practice of data science. We’ve put together a list of primary reasons why data science projects fail – and a few strategies for forging success in the future – to help you avoid similar mistakes.

Hurdles

You lack analytical maturity.

Many organizations are antsy to predict events or decipher buyer motivations without having first developed the proper structure, data quality, and data-driven culture. And that overzealousness is a recipe for disaster. While a successful data science project will take some time, a well-thought-out data science strategy can ensure you will see value along the way to your end goal.

Effective analytics only happens through analytical maturity. That’s why we recommend organizations conduct a thorough current state analysis before they embark on any data science project. In addition to evaluating the state of their data ecosystem, they can determine where their analytics falls along the following spectrum:

Descriptive Analytics: This type of analytics is concerned with what happened in the past. It mainly depends on reporting and is often limited to a single or narrow source of data. It’s the ground floor of potential analysis.

Diagnostic Analytics: Organizations at this stage are able to determine why something happened. This level of analytics delves into the early phases of data science but lacks the insight to make predictions or offer actionable insight.

Predictive Analytics: At this level, organizations are finally able to determine what could happen in the future. By using statistical models and forecasting techniques, they can begin to look beyond the present into the future. Data science projects can get you into this territory.

Prescriptive Analytics: This is the ultimate goal of data science. When organizations reach this stage, they can determine what they should do based on historical data, forecasts, and the projections of simulation algorithms.

Your project doesn’t align with your goals.

Data science, removed from your business objectives, always falls short of expectations. Yet in spite of that reality, many organizations attempt to harness machine learning, predictive analytics, or any other data science capability without a clear goal in mind. In our experience, this happens for one of two reasons:

1. Stakeholders want the promised results of data science but don’t understand how to customize the technologies to their goals. This leads them to pursue a data-driven framework that’s prevailed for other organizations while ignoring their own unique context.

2. Internal data scientists geek out over theoretical potential and explore capabilities that are stunning but fail to offer practical value to the organization.

Outside of research institutes or skunkworks programs, exploratory or extravagant data science projects have a limited immediate ROI for your organization. In fact, the odds are very low that they’ll pay off. It’s only through a clear vision and practical use cases that these projects are able to garner actionable insights into products, services, consumers, or larger market conditions.

Every data science project needs to start with an evaluation of your primary goals. What opportunities are there to improve your core competency? Are there any specific questions you have about your products, services, customers, or operations? And is there a small and easy proof of concept you can launch to gain traction and master the technology?

The above use case from GE is a prime example of having a clear goal in mind. The multinational company was in the middle of restructuring, reemphasizing its focus on aero engines and power equipment. With the goal of reducing their six- to 12-month design process, they decided to pursue a machine learning project capable of increasing the efficiency of product design within their core verticals. As a result, this project promises to decrease design time and budget allocated for R&D.

Organizations that embody GE’s strategy will face fewer false starts with their data science projects. For those that are still unsure about how to adapt data-driven thinking to their business, an outsourced partner can simplify the selection process and optimize your outcomes.

Your solution isn’t user-friendly.

The user experience is often an overlooked aspect of viable data science projects. Organizations do all the right things to create an analytics powerhouse customized to solve a key business problem, but if the end users can’t figure out how to use the tool, the ROI will always be weak. Frustrated users will either continue to rely upon other platforms that provided them with limited but comprehensible reporting capabilities, or they will stumble through the tool without unlocking its full potential.

Your organization can avoid this outcome by involving a range of end users in the early stages of project development. This means interviewing both average users and extreme users. What are their day-to-day needs? What data are they already using? What insight do they want but currently can’t obtain?

An equally important task is to determine your target user’s data literacy. The average user doesn’t have the ability to derive complete insights from the represented data. They need visualizations that present a clear-cut course of action. If the data scientists are only thinking about how to analyze complex webs of disparate data sources and not whether end users will be able to decipher the final results, the project is bound to struggle.

You don’t have data scientists who know your industry.

Even if your organization has taken all of the above considerations into mind, there’s still a chance you’ll be dissatisfied with the end results. Most often, it’s because you aren’t working with data science consulting firms that comprehend the challenges, trends, and primary objectives of your industry.

Take healthcare, for example. Data scientists who only grasp the fundamentals of machine learning, predictive analytics, or automated decision-making can only provide your business with general results. The right partner will have a full grasp of healthcare regulations, prevalent data sources, common industry use cases, and what target end users will need. They can address your pain points and already know how to extract full value for your organization.

And here’s another example from one of our own clients. A Chicago-based retailer wanted to use their data to improve customer lifetime value, but they were struggling with a decentralized and unreliable data ecosystem. With the extensive experience of our retail and marketing team, we were able to outline their current state and efficiently implement a machine-learning solution that empowered our client. As a result, our client was better able to identify sales predictors and customize their marketing tactics within their newly optimized consumer demographics. Our knowledge of their business and industry helped them to get the full results now and in the future.

Is your organization equipped to achieve meaningful results through data science? Secure your success by working with 2nd Watch. Schedule a whiteboard session with our team to get you started on the right path.

Is Your Business Ready for Data Science? 8 Tips Can Up Your ROI

Enhanced predictions. Dynamic forecasting. Increased profitability. Improved efficiency. Data science is the master key to unlock an entire world of benefits. But is your business even ready for data science solutions? Or more importantly, is your business ready to get the full ROI from data science?

Let’s look at the overall market for some answers. Most organizations have increased their ability to use their data to their advantage in recent years. BCG surveys have shown that the average organization has moved beyond the “developing” phase of data maturity into a “mainstream” phase. This means more organizations are improving their analytics capabilities, data governance, data ecosystems, and data science use cases. However, there’s still a long way to go until they are maximizing the value of their data.

Looking to get started with ML, AI, or other data science initiatives? Learn how with our Data Science Readiness Assessment.

So, yes, there is a level of functional data science that many organizations are exploring and capable of reaching. Yet if you want to leverage data science to deliver faster and more complete insights (and ROI), your business needs to ensure that the proper data infrastructure and the appropriate internal culture exist.

The following eight tips will help your machine learning projects, predictive analytics, and other data science initiatives operate with greater efficiency and speed. Each of these tips will require an upfront investment of time and money, but they are fundamental in making sure your data science produces the ROI you want.

Laying the Right Foundation with Accurate, Consistent, and Complete Data

Tip 1: Before diving into data science, get your data in order.
Raw data, left alone, is mostly an unruly mess. It’s collected by numerous systems and end users with incongruous attention to detail. After it’s gathered, the data is often subject to migrations, source system changes, or unpredictable system errors that alter the quality even further. While you can conduct data science projects without first focusing on proper data governance, what ends up on your plate will vary greatly – and comes with a fair amount of risk.

Consider this hypothetical example of predictive analytics in manufacturing. A medium-sized manufacturer wants to use predictive maintenance to help lower the risk and cost of an avoidable machine breakdown (which can easily amount to $22,000 per minute). But first, they need to train a machine learning algorithm to predict impending breakdowns using their existing data. If the data’s bad, then the resulting detection capabilities might result in premature replacements or expensive disruptions.

Tip 2: Aim to create a single source of truth with your data.
Unifying data from assorted sources into a modern data warehouse or data mart simplifies the entire analytical process. Organizations should always start by implementing data ingestion best practices to extract and import high-quality data into the destination source. From there, it’s critical to build a robust data pipeline that maintains the flow of quality data into your warehouse.

Tip 3: Properly cleanse and standardize your data.
Each department in your organization has its own data sources, formats, and definitions. Before your data can be data science-ready and generate accurate predictions, it must be cleansed, standardized, and devoid of duplicates before it ever reaches your analytics platform or data science tool. Only through effective data cleansing and data governance strategy can you reach that level.

Tip 4: Don’t lean on your data scientist to clean up the data.
Sure, data scientists are capable of cleaning up and preparing your data for data science, but pulling them into avoidable data manipulation tasks slows down your analytical progress and impacts your data science initiatives. Leaning on your data scientist to complete these tasks can also lead to frustrated data scientists and increase turnover.

It’s not that data scientists shouldn’t do some data cleansing and manipulation from time to time; it’s that they should only be doing it when it’s necessary.

Tip 5: Create a data-driven culture.
Your data scientist or data science consulting partner can’t be the only ones with data on the mind. Your entire team needs to embrace data-driven habits and practices, or your organization will struggle to obtain meaningful insights from your data.

Frankly, most businesses have plenty of room to grow in this regard. For those looking to implement a data-driven culture before they forge deep into the territory of data science, you need to preach from the top down – grassroots data implementations will never take hold. Your primary stakeholders need to believe not only in the possibility of data science but in the cultivation of practices that fortify robust insights.

A member of your leadership team, whether a chief data officer or another senior executive, needs to ensure that your employees adopt data science tools, observe habits that foster data quality, and connect business objectives to this in-depth analysis.

Tip 6: Train your whole team on data science.
Data science is no longer just for data scientists. A variety of self-service tools and platforms have allowed ordinary end users to leverage machine learning algorithms, predictive analytics, and similar disciplines in unprecedented ways.

With the right platform, your team should be able to conduct sophisticated predictions, forecasts, and reporting to unlock rich insight from their data. What that takes is the proper training to acclimate your people to their newfound capabilities and show the practical ways data science can shape their short- and long-term goals.

Tip 7: Keep your data science goals aligned with your business goals.
Speaking of goals, it’s just as important for data-driven organizations to inspect the ways in which their advanced analytical platforms connect with their business objectives. Far too often, there’s disconnection and data science projects either prioritize lesser goals or pursue abstract and impractical intelligence. If you determine which KPIs you want to improve with your analytical capabilities, you have a much better shot at eliciting the maximum results for your organization.

Tip 8: Consider external support to lay the foundation.
Though these step-by-step processes are not mandatory, focusing on creating a heartier and cleaner data architecture as well as a culture that embraces data best practices will set you in the right direction. Yet it’s not always easy to navigate on your own.

With the help of data science consulting partners, you can make the transition in ways that are more efficient and gratifying in the long run.

Conclusion

Need some support getting your business ready for data science? 2nd Watch’s team of data management, analytics, and data science consultants can help you ensure success with your data science initiatives from building the business case and creating a strategy to data preparation and building models.

Schedule a data science readiness whiteboard session with our team and we’ll determine where you’re at and your full potential with the right game plan.

How Insurance Fraud Analytics Can Protect Your Business from Fraudulent Claims

With your experience in the insurance industry, you understand more than most about how the actions of a smattering of people can cause disproportionate damage. The $80 billion in fraudulent claims paid out across all lines of insurance each year, whether soft or hard fraud, is perpetrated by lone individuals, sketchy auto mechanic shops, or the occasional organized crime group. The challenge for most insurers is that detecting, investigating, and mitigating these deceitful claims is a time-consuming and expensive process.

Rather than accepting loss to fraud as part of the cost of doing business, some organizations are enhancing their detection capabilities with insurance analytics solutions. Here is how your organization can use insurance fraud analytics to enhance fraud detection, uncover emerging criminal strategies, and still remain compliant with data privacy regulations.

Recognizing Patterns Faster

When you look at exceptional claim’s adjusters or special investigation units, one of the major traits they all share is an uncanny ability to recognize fraudulent patterns. Their experience allows them to notice the telltale signs of fraud, whether it’s frequent suspicious estimates from a body shop or complex billing codes intended to hide frivolous medical tests. Though you trust adjusters, many rely on heuristic judgments (e.g., trial and error, intuition, etc.) rather than hard rational analysis. When they do have statistical findings to back them up, they struggle to keep up with the sheer volume of claims.

This is where machine learning techniques can help to accelerate pattern recognition and optimize the productivity of adjusters and special investigation units. An organization starts by feeding a machine learning model a large data set that includes verified legitimate and fraudulent claims. Under supervision, the machine learning algorithm reviews and evaluates the patterns across all claims in the data set until it has mastered the ability to spot fraud indicators.

Let’s say this model was given a training set of legitimate and fraudulent auto insurance claims. While reviewing the data for fraud, the algorithm might spot links in deceptive claims between extensive damage in a claim and a lack of towing charges from the scene of the accident. Or it might notice instances where claims involve rental cars rented the day of the accident that are all brought to the same body repair shop. Once the algorithm begins to piece together these common threads, your organization can test the model’s unsupervised ability to create a criteria for detecting deception and spot all instances of fraud.

What’s important in this process is finding a balance between fraud identification and instances of false positives. If your program is overzealous, it might create more work for your agents, forcing them to prove that legitimate claims received an incorrect label. Yet when the machine learning model is optimized, it can review a multitude of dimensions to identify the likelihood of fraudulent claims. That way, if an insurance claim is called into question, adjusters can comb through the data to determine if the claim should truly be rejected or if the red flags have a valid explanation.

Detecting New Strategies

The ability of analytics tools to detect known instances of fraud is only the beginning of their full potential. As with any type of crime, insurance fraud evolves with technology, regulations, and innovation. With that transformation comes new strategies to outwit or deceive insurance companies.

One recent example has emerged through automation. When insurance organizations began to implement straight through processing (STP) in their claim approvals, the goal was to issue remittances more quickly, easily, and cheaply than manual processes. For a time, this approach provided a net positive, but once organized fraudsters caught wind of this practice, they pounced on a new opportunity to deceive insurers.

Criminals learned to game the system, identifying amounts that were below the threshold for investigation and flying their fraudulent claims under the radar. In many cases, instances of fraud could potentially double without the proper tools to detect these new deception strategies. Though most organizations plan to enhance their anti-fraud technology, there’s still the potential for them to lose millions in errant claims – if their insurance fraud analytics are not programmed to detect new patterns.

In addition to spotting red flags for common fraud occurrences, analytics programs need to be attuned to any abnormal similarities or unlikely statistical trends. Using cluster analysis, an organization can detect statistical outliers and meaningful patterns that reveal potential instances of fraud (such as suspiciously identical fraud claims).

Even beyond the above automation example, your organization can use data discovery to find hidden indicators of fraud and predict future incidents. Splitting claims data into various groups through a few parameters (such as region, physician, billing code, etc., in healthcare) can help in detecting unexpected correlations or warning signs for your automation process or even human adjusters to flag as fraud.

Safeguarding Personally Identifiable Information

As you work to improve your fraud detection, there’s one challenge all insurers face: protecting the personally identifiable information (PII) of policyholders while you analyze your data. The fines related to HIPAA violations can amount to $50,000 per violation, and other data privacy regulations can result in similarly steep fines. The good news is that insurance organizations can balance their fraud prediction and data discovery with security protocols if their data ecosystem is appropriately designed.

Maintaining data privacy compliance and effective insurance fraud analytics requires some maneuvering. Organizations that derive meaningful and accurate insight from their data must first bring all of their disparate data into a single source of truth. Yet, unless they also implement access control through a compliance-focused data governance strategy, there’s a risk of regulatory violations while conducting fraud analysis.

One way to limit your exposure is to create a data access layer that tokenizes the data, replacing any sensitive PII with unique identification symbols to keep data separate. Paired with clear data visualization capabilities, your adjusters and special investigation units can see clear-cut trends and evolving strategies without revealing individual claimants. From there, they can take their newfound insights into any red flag situation, saving your organization millions while reducing the threat of noncompliance.

Want to learn more about how the right analytics solutions can help you reduce your liability, issue more policies, and provide better customer service? Check out our insurance analytics solutions page for use cases that are transforming your industry.

Supply Chain Industry Using Predictive Analytics to Boost Their Competitive Edge

Professionals in the supply chain industry need uncanny reflexes. The moment they get a handle on raw materials, labor expenses, international legislation, and shipping conditions, the ground shifts beneath them and all the effort they put into pushing their boulder up the hill comes undone. With the global nature of today’s supply chain environment, the factors governing your bottom line are exceptionally unpredictable. Fortunately, there’s a solution for this problem: predictive analytics for supply chain management.

This particular branch of analytics offers an opportunity for organizations to anticipate challenges before they happen. Sounds like an indisputable advantage, yet only 30% of supply chain professionals are using their data to forecast their future.

Want to improve your supply chain operations and better understand your customer’s behavior? Learn about our demand forecasting data science starter kit.

Though most of the stragglers plan to implement predictive analytics in the next 10 years, they are missing incredible opportunities in the meantime. Here are some of the competitive advantages companies are missing when they choose to ignore predictive operational analytics.

Enhanced Demand Forecasting

How do you routinely hit a moving goalpost? As part of an increasingly complex global system, supply chain leaders are faced with an increasing array of expected and unexpected sales drivers from which they are pressured to determine accurate predictions about future demand. Though traditional demand forecasting yields some insight from a single variable or small dataset, real-world supply chain forecasting requires tools that are capable of anticipating demand based on a messy, multifaceted assembly of key motivators. Otherwise, they risk regular profit losses as a result of the bullwhip effect, buying far more products or raw materials than are necessary.

For instance, one of our clients, an international manufacturer, struggled to make accurate predictions about future demand using traditional forecasting models. Their dependence on the historical sales data of individual SKUs, longer order lead times, and lack of seasonal trends hindered their ability to derive useful insight and resulted in lost profits. By implementing machine learning models and statistical packages within their organization, we were able to help them evaluate the impact of various influencers on the demand of each product. As a result, our client was able to achieve an 8% increase in weekly demand forecast accuracy and 12% increase in monthly demand forecast accuracy.

This practice can be carried across the supply chain in any organization, whether your demand is relatively predictable with minor spikes or inordinately complex. The right predictive analytics platform can clarify the patterns and motivations behind complex systems to help you to create a steady supply of products without expensive surpluses.

Smarter Risk Management

The modern supply chain is a precise yet delicate machine. The procurement of raw materials and components from a decentralized and global network has the potential to cut costs and increase efficiencies – as long as the entire process is operating perfectly. Any type of disruption or bottleneck in the supply chain can create a massive liability, threatening both customer satisfaction and the bottom line. When organizations leave their fate up to reactive risk management practices, these disruptions are especially steep.

Predictive risk management allows organizations to audit each component or process within their supply chain for its potential to destabilize operations. For example, if your organization currently imports raw materials such as copper from Chile, predictive risk management would account for the threat of common Chilean natural disasters such as flooding or earthquakes. That same logic applies to any country or point of origin for your raw materials.

You can evaluate the cost and processes of normal operations and how new potentialities would impact your business. Though you can’t prepare for every possible one of these black swan events, you can have contingencies in place to mitigate losses and maintain your supply chain flow.

Formalized Process Improvement

As with any industry facing internal and external pressures to pioneer new efficiencies, the supply chain industry cannot rely on happenstance to evolve. There needs to be a twofold solution in place. One, there needs to be a culture of continuous organizational improvement across the business. Two, there need to be apparatuses and tools in place to identify opportunities and take meaningful action.

For the second part, one of the most effective tools is predictive analytics for supply chain management. Machine learning algorithms are exceptional at unearthing inefficiencies or bottlenecks, giving stakeholders the fodder to make informed decisions. Because predictive analytics removes most of the grunt work and exploration associated with process improvement, it’s easier to create a standardized system of seeking out greater efficiencies. Finding new improvements is almost automatic.

Ordering is an area that offers plenty of opportunities for improvement. If there is an established relationship with an individual customer (be it retailer, wholesaler, distributor, or the direct consumer), your organization has stockpiles of information on individual and demographic customer behavior. This data can in turn be leveraged alongside other internal and third-party data sources to anticipate product orders before they’re made. This type of ordering can accelerate revenue generation, increase customer satisfaction, and streamline shipping and marketing costs.

3 Benefits of Machine Learning for Retail

You already know that data is a gateway for retailers to improve customer experiences and increase sales. Through traditional analysis, we’ve been able to combine a customer’s purchase history with their browser behavior and email open rates to help pinpoint their current preferences and meet their precise future needs. Yet the new wave of buzzwords such as “machine learning” and “AI” promise greater accuracy and personalization in your forecasts and the marketing actions they inform.

What distinguishes the latest predictive analytics technology from the traditional analytics approach? Here are three of the numerous examples of this technology’s impact on addressing retail challenges and achieving substantial ROI.

Want better dashboards? Our data and analytics experts are here to help. Learn more about our data visualization starter pack.

1. Increase customer lifetime value.

Repeat customers contribute to 40% of a brand’s revenue. But how do you know where to invest your marketing dollars to increase your customer return rate? All of this comes down to predicting which customers are most likely to return and factors that influence the highest customer lifetime value (CLV) for these customers, which are both great use cases for machine learning.

Consider this example: Your customer is purchasing a 4K HD TV and you want to predict future purchases. Will this customer want HD accessories, gaming systems, or an upgraded TV in the near future? If they are forecasted to buy more, which approach will work to increase their chances of making the purchase through you? Predictive analytics can provide the answer.

One of the primary opportunities is to create more personalized sales process without mind-boggling manual effort. The sophistication of machine learning algorithms allows you to quickly review large inputs on purchase histories, internet and social media behavior, customer feedback, production costs, product specifications, market research, and other data sources with accuracy.

Historically, data science teams had to run one machine-learning algorithm at a time. Now, modern solutions from providers like DataRobot allows a user to run hundreds of algorithms at once and even identify the most applicable ones. This vastly increases the time-to-market and focuses your expensive data science team’s hours on interpreting results rather than just laying groundwork for the real work to begin.

2. Attract new customers.

Retailers cannot depend on customer loyalty alone. HubSpot finds that consumer loyalty is eroding, with 55% of customers no longer trusting the companies they buy from. With long-running customers more susceptible to your competitors, it’s important to always expand your base. However, as new and established businesses vie for the same customer base, it also appears that customer acquisition costs have risen 50% in five years.

Machine learning tools like programmatic advertising offer a significant advantage. For those unfamiliar with the term, programmatic advertising is the automated buying and selling of digital ad space using intricate analytics. For example, if your business is attempting to target new customers, the algorithms within this tool can analyze data from your current customer segments, page context, and optimal viewing time to push a targeted ad to a prospect at the right moment.

Additionally, businesses are testing out propensity modeling to target consumers with the highest likelihood of customer conversion. Machine learning tools can score consumers in real time using data from CRMs, social media, e-commerce platforms, and other sources to identify the most promising customers. From there, your business can personalize their experience to better shepherd them through the sales funnel – even going as far as reducing cart abandon rates.

3. Automate touch points.

Often, machine learning is depicted as a way to eliminate a human workforce. But that’s a mischaracterization. Its greatest potential lies in augmenting your top performers, helping them automate routine processes to free up their time for creative projects or in-depth problem-solving.

For example, you can predict customer churn based on irregularities in buying behavior. Let’s say that a customer who regularly makes purchases every six weeks lapses from their routine for 12 weeks. A machine learning model can identify if their behavior is indicative of churn and flag customers likely not to return. Retailers can then layer these predictions with automated touch points such as sending a reminder about the customer’s favorite product – maybe even with a coupon – straight to their email to incentivize them to return.

How to Get Started

Though implementing machine learning can transform your business in many ways, your data needs to be in the right state before you can take action. That involves identifying a single customer across platforms, cleaning up the quality of your data, and identifying specific use cases for machine learning. With the right partner, you can not only make those preparations but rapidly reap the rewards of powering predictive analytics with machine learning.

Want to learn how the 2nd Watch team can apply machine learning to your business? Contact us now.

How Machine Learning Can Save You Millions on Server Capacity

TL;DR

While most servers spend the majority of their time well below peak usage, companies often pay for max usage 24/7.
Cloud providers enable the ability to scale usage up and down, but determining the right schedule is highly prone to human error.
Machine learning models can be used to predict server usage throughout the day and scale the servers to that predicted usage.
Depending on the number of servers, savings can be in the millions of dollars.

How big of a server do you need? Do you know? Enough to handle peak load, plus a little more headroom? How often is your server going to run at peak utilization? For two hours per day? Ten hours? If your server is only running at two hours per day at peak load, then you are paying for 22 hours of peak performance that you aren’t using. Multiply that inefficiency across many servers, and that’s a lot of money spent on compute power sitting idle.

Cloud Providers Make Scaling Up and Down Possible (with a Caveat)

If you’ve moved off-premise and are using a cloud provider such as AWS or Azure, it’s easy to reconfigure server sizes if you find that you need a bigger server or if you’re not fully utilizing the compute, as in the example above. You can also schedule these servers to resize if there are certain times where the workload is heavier. For example, scheduling a server to scale up during nightly batch processes or during the day to handle customer transactions.

The ability to schedule is powerful, but it can be difficult to manage the specific needs of each server, especially when your enterprise uses many servers for a wide variety of purposes. The demands of a server can also change, perhaps without their knowledge, requiring close monitoring of the system. Managing the schedules of servers becomes yet another task to pile on top of all of IT’s other responsibilities. If only there was a solution that could recognize the needs of a server and create dynamic schedules accordingly, and do so without any intervention from IT. This type of problem is a great example for the application of machine learning.

How Machine Learning Can Dynamically Scale Your Server Capacity (without the Guesswork)

Machine learning excels at taking data and creating rules. In this case, you could use a model to predict server utilization, and then use that information to dynamically create schedules for each database.

Server Optimization In Action

We’ve previously done such an application for a client in the banking industry, leading to a 68% increase in efficiency and a cost savings of $10,000 per year for a single server. When applied to the client’s other 2,000 servers, this method could lead to savings of $20 million per year!

While the actual savings will depend on the number of servers employed and the efficiency at which they currently run, the cost benefits will be significant once the machine learning server optimization model is applied.

If you’re interested in learning more about using machine learning to save money on your server usage, click here to contact us about our risk-free server optimization whiteboard session.

Analytics and Insights for Marketers

Analytics & Insights for Marketers is the third in a series of our Marketers’ Guide to Data Management and Analytics. In this series, we cover major terms, acronyms, and technologies you might encounter as you seek to take control of your data, improve your analytics, and get more value from your MarTech investments.

In case you missed them, you can access part one here and part two here.

In this post, we’ll explore:

Business intelligence (BI)
Real-time analytics
Embedded analytics
Artificial intelligence (AI)
Machine learning

Business Intelligence

Business intelligence refers to the process in which data is prepared and analyzed to provide actionable insights and help users make informed decisions. It often encompasses various forms of visualizations in dashboards and reports that answer key business questions.

Why It Matters for Marketers:

With an increasing number of marketing channels comes an increasing amount of marketing data. Marketers who put BI tools to use gain essential insights faster, more accurately define key demographics, and make marketing dollars last.

Marketers without access to a BI tool spend a disproportionate amount of time preparing, rather than analyzing, their data. With the right dashboards in place, you can visualize observations about customer and demographic behaviors in the form of KPIs, graphs, and trend charts that inform meaningful and strategic campaigns.

Real-World Examples:

Your BI dashboards can help answer common questions about more routine marketing metrics without spending hours preparing the data. In a way, they take the pulse of your marketing initiatives. Which channels bring in the most sales? Which campaigns generate the most leads? How do your retention rate and ROI compare over time? Access to these metrics and other reports can shape the big picture of your campaigns. They help you make a measurable impact on your customer lifetime value, marketing RPI, and other capabilities.

Real-Time Analytics

Real-time analytics utilizes a live data stream and frequent data refreshes to enable immediate analysis as soon as data becomes available.

Why It Matters for Marketers:

Real-time analytics enhances your powers of perception by providing up-to-the-minute understanding of buyers’ motivations. A real-time analytics solution allows you to track clicks, web traffic, order confirmations, social media posts, and other events as they happen, enabling you to deliver seamless responses.

Real-World Examples:

Real-time analytics can be used to reduce cart abandonment online. Data shows that customers abandon 69.57% of online transactions before they are completed. Implementing a real-time analytics solution can enable your marketing team to capture these lost sales.

By automatically evaluating a combination of live data (e.g., abandonment rates, real-time web interactions, basket analysis, etc.) and historical data (e.g., customer preferences, demographic groups, customer click maps, etc.), you can match specific customers to targeted messaging, right after they leave your site.

Embedded Analytics

Embedded analytics is the inclusion of a business intelligence functionality (think graphs, charts, and other visualizations) within a larger application (like your CRM, POS, etc.)

Why It Matters for Marketers:

The beauty of embedded analytics is that you do not need to open up a different interface to visualize data or run reports. Integrated BI functionality enables you to review customer data, sales history, or conversion rates along with relevant reports that enhance your decision-making. This enables you to reduce time-to-insight and empower your team to make data-driven decisions without leaving the applications they use daily.

Real-World Examples:

A member of your marketing team is reviewing individual customers in your CRM to analyze their customer lifetime value. Rather than exporting the data into a different analytics platform, you can run reports directly in your CRM – and even incorporate data from external sources.

In doing so, you can identify different insights that improve campaign effectiveness such as which type of content best engages your customers, how to re-engage detractors, or when customers expect personalized content.

Artificial Intelligence

AI is the ability for computer programs or machines to learn, analyze data, and make autonomous decisions without any major contributions from humans.

Why It Matters for Marketers:

Implementing AI can provide a better understanding of your business as it detects forward-looking data patterns that employees would struggle to find – and in a fraction of the time. Additionally, marketers can improve customer service through a data-driven understanding of customer behavior and with new AI-enabled services like chatbots.

Real-World Examples:

Customizing email messaging used to be a laborious process. You’d need to manually create a number of campaigns. Even then, you could only tailor your messages to segments, not to a specific customer. Online lingerie brand Adore Me pursued AI to mine existing customer information and histories to create personalized messages across omnichannel communications. As a result, monthly revenue increased by 15% and the average order amount increased by 22%.

AI chatbots are also making waves, and Sephora is a great example. The beauty brand launched a messaging bot through Kik as a way of engaging with their teenage customers preparing for prom. The bot provided them with tailored makeup tutorials, style guides, and other related video content. During the campaign, Sephora had more than 600,000 interactions and received 1,500 questions that they answered on Facebook Live.

Machine Learning

Machine learning is a method of data analysis in which statistical models are built and updated in an automated process.

Why It Matters for Marketers:

Marketers have access to a growing volume and variety of complex data that doesn’t always provide intuitive insight at first glance. Machine learning algorithms not only accelerate your ability to analyze data and find patterns, but they can identify unforeseeable connections that a human user might have missed. Through machine learning, you can enhance the accuracy of your analyses and dig deeper into customer behavior.

Real-World Examples:

One Chicago retailer used a centralized data platform and machine learning to identify patterns and resolve questions about customer lifetime value. In an increasingly competitive landscape, their conventional reporting solution wasn’t cutting it.

By combining data from various sources and then performing deeper, automated analysis, they were able to anticipate customer behavior in unprecedented ways. Machine learning enabled them to identify which types of customers would lead to the highest lifetime value, which customers had the lowest probability of churn, and which were the cheapest to acquire. This led to more accurate targeting of profitable customers in the market.

That’s only the beginning: a robust machine learning algorithm could even help predict spending habits or gather a customer sentiment analysis based on social media activity. Machine learning processes data much faster than humans and is able to catch nuances and patterns that are undetectable to the naked eye.

We hope you gained a deeper understanding into the various ways to analyze your data to receive business insights. Feel free to contact us with any questions or to learn more about what analytics solution would work best for your organizational needs.