What Is Sisu? An Intro to Sisu via Data Analytics in the Telecom Industry

Sisu is a fairly new, relatively unique tool that applies a user-friendly interface to robust and deep-diving business analytics, such as the example of big data analytics in the telecom industry we’ll cover in this blog post. With well-defined KPIs and a strong grasp of the business decisions relying on the analytics, even non-technical users are able to confidently answer questions using the power of machine learning through Sisu.

Below, we’ll detail the process of using Sisu to uncover the main drivers of customer churn for a telecom company, showing you what kind of data is appropriate for analysis in Sisu, what analysis 2nd Watch has performed using Sisu, and what conclusions our client drew from the data analysis. Read on to learn how Sisu may offer your organization the competitive advantage you’re looking for.

What is Sisu?

Sisu uses a high-level declarative query model to allow users to tap into existing data lakes and identify the key features impacting KPIs, even enabling users who aren’t trained data analysts or data scientists. Analysis improves with time as data increases and more users interact with Sisu’s results.

Sisu moves from user-defined objectives to relevant analysis in five steps:

  1. Querying and Processing Data: Sisu ingests data from a number of popular platforms (e.g., Amazon Redshift, BigQuery, Snowflake) with light transformation and can update/ingest over time.
  2. Data Quality, Enrichment, and Featurization: Automated, human-readable featurization exposes the most relevant statistical factors.
  3. Automated Model and Feature Selection: Sisu trains multiple models to investigate KPIs on a continuous or categorical basis.
  4. Personalized Ranking and Relevance: Sisu ranks facts by several measures that prioritize human time and attention, improving the personalized model over time.
  5. Presentation and Sharing: To dig into facts, Sisu offers natural language processing (NLP), custom visualization, supporting statistics, and related facts that illustrate why a fact was chosen.

How does Sisu help users leverage data to make better data-driven decisions?

Sisu can help non-technical users analyze data from various data sources (anything from raw data in a CSV file to an up-and-running database), improving data-driven decision-making across your organization. A couple of things to keep in mind: the data should already be cleaned and of high integrity; and Sisu works best with numerical data, not text-based data.

Once the data is ready for analysis, you can easily create a simple visualization:

  1. Identify your key variable.
  2. Choose a tracking metric.
  3. Select the time frame, if applicable.
  4. Run the visualization and apply to A/B groups as necessary.

With Sisu, users don’t need to spend time on feature selection. When a user builds a metric, Sisu queries the data, identifies high-ranking factors, and presents a list of features with the most impact. This approach subverts the traditional OLAP and BI process, making it easier and faster to ask the right questions and get impactful answers – requiring less time while offering more value.

Simplicity and speed are key contributors to why Sisu is so advantageous, from both a usability standpoint and a financial point of view. Sisu can help you increase revenue and decrease expenses with faster, more accurate analytics. Plus, because Sisu puts the ability to ask questions in the hands of non-technical users, it creates more flexibility for teams throughout your organization.

How did 2nd Watch use Sisu to reduce customer churn for a telecom company?

Being able to pick out key drivers in any set of data is essential for users to develop specific business-impacting insights. Instead of creating graphics from scratch or analyzing data through multiple queries like other analytical tools require, Sisu allows your teams to query their data in a user-friendly way that delivers the answers they need.

For our client in the telecommunications industry, group comparisons were crucial in determining who would likely become long-standing customers and who would have a higher rate of churn. Filtering and grouping the demographics of our client’s customer base allowed them to outline their target market and begin understanding what attracts individuals to stay longer. Of course, this then enables the company to improve customer retention – and ultimately revenue.

Sisu can also be employed in other areas of our client’s organization. In addition to customer churn data, they can investigate margins, sales, network usage patterns, network optimization, and more. With the large volumes of data in the telecom industry, our client has many opportunities to improve their services and solutions through the power of Sisu’s analytics.

How can Sisu benefit your organization?

Sisu reduces barriers to high-level analytical work because its automated factor selection and learning capabilities make analytics projects more efficient. Using Sisu to focus on who is driving business-impacting events (like our telecom client’s customer churn) allows you to create user profiles, monitor those profiles, and track goals and tweak KPIs accordingly. In turn, this allows you to be more agile, move from reactive to proactive, and ultimately increase revenue.

Because feature selection is outsourced to Sisu’s automated system, Sisu is a great tool for teams lacking in high-level analytics abilities. If you’re hoping to dive into more advanced analytics or data science, Sisu could be the stepping stone your team needs.

Learn more about 2nd Watch’s data and analytics solutions or contact us to discuss how we can jumpstart your organization’s analytics journey.

By Sarah Dudek, 2nd Watch Data Insights Consultant

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28 Questions to Ask During Due Diligence to Accelerate Data Value Creation

Data and analytics is a major driver and source of great value for private equity firms. The best private equity firms know the full power of data and analytics. They realize that portfolio company enterprise data is typically the crown jewel of an acquisition or deal target.

Data and analytics are also the foundation of financial and operational transformation. Quickly pulling data from their portfolio companies, and consolidating it into actionable information, will enable and accelerate financial and operational value opportunities, driving up EBITDA. Even better, the creation of data monetization revenue opportunities unlocks hidden sources of value creation. And down the road, a data-driven organization will always yield much higher financial valuations and returns to their investors.

Due Diligence to Accelerate Data Value Creation

Most firms doing due diligence on potential targets will only do basic due diligence. They will focus on assuring financial valuation and risk assessment. Therefore, most PE firms will conduct standard IT due diligence, analyzing expense budgets, hardware and software capital assets, license and service contracts, and headcount/staffing. They will seek to understand IT architecture, as well as assess the network in terms of capability. Because it is top of mind, the due diligence effort will also heavily focus on cyber and network security, and the architecture built to protect the portfolio company and its data. Typically, they will declare the due diligence effort complete.

Beyond classical IT due diligence, most dealmakers try to understand their data assets once the deal has closed and they begin operating the acquired company. However, best practice says otherwise. To accelerate the data and analytics value creation curve, it really starts at data due diligence. Precise data due diligence serves as the foundation for portfolio data strategy, as well as uncovers hidden sources of potential and opportunistic strategic value. Doing data due diligence will give the PE firm and portfolio company a running start on data value creation once the deal has closed.

What should deal firms look for when doing data and analytics due diligence? Here are key areas and questions for investigation and analysis when investigating a target portfolio company.

 

Step 1: Determine the target company’s current overall approach to managing and analyzing its data.

Develop an understanding of the target company’s current approach to accessing and analyzing their data. Understanding their current approach will let you know the effort needed to accelerate potential data value creation.

  1. Does the target company have a comprehensive data strategy to transform the company into a data-driven enterprise?
  2. Does the company have a single source of truth for data, analytics, and reporting?
  3. What is the target company’s usage of data-driven business decisions in operations, marketing, sales, and finance?
  4. What cloud services, architectures, and tools does the company use to manage its data?
  5. What is the on-prem data environment and architecture?
  6. What kind of cloud data and analytics proofs-of-concept does the company have in place to build out its capabilities?
  7. Has the company identified and implemented value prop use cases for data and analytics, realizing tangible ROI?
  8. Where is the target company on the data and analytics curve?

Step 2: Identify the data sources, what data they contain, and how clean the data is.

Data value depends on breadth and quality of the target company’s data and data sources. Document what the data sources are, what purpose they serve, how the target company currently integrates data sources for analytics, the existing security and data governance measures, and the overall quality of the data.

  1. Inventory all of the company’s data sources, including a data dictionary, size, physical and logical location, data architecture, data model, etc.
  2. How many of the data sources have an API for ETL (extract, transform, load) to pull data into the data warehouse?
  3. Does the target company have a data warehouse, and are all of its data sources feeding the data warehouse?
  4. How much history does each data source have? Obviously, the longer the history, the greater the value of the data source.
  5. What kind of data security is in place to protect all data sources?
  6. What kind of data quality assessment for each source has been conducted?

Step 3: Assess the quality of the target company’s analytics and reporting.

Review how the target company approaches reporting and analytics. This step should include a review of their tools and technologies, KPIs and metrics, and reporting (i.e., self- service, interactive, dashboards, Excel reports, reports delivered by IT, etc.).

  1. What kind of reporting does the company use?
  2. Does the portfolio company have a heavy dependence on Excel for producing reports?
  3. Describe the KPIs that are in place for each functional area. How has the company been tracking against these KPIs?
  4. Does the company enable self-service analytics across the enterprise?
  5. What is the inventory of all reports generated by the company?
  6. What percentage of the reports are delivered by way of dashboarding?

Step 4: Review the people and processes involved in data management and analytics.

Determine the extent of the target company as a data-driven organization by examining the people and processes behind the data strategy. Document which FTEs are involved with data and analytics, how much time is dedicated to reporting and report development, as well as the current processes for analytics.

  1. How many FTEs are engaged in financial and operational report development?
  2. What does the data and analytics team consist of, in terms of data engineers, data scientists, data administrators, and others with data titles?
  3. What kind of data governance is in place for the target company to regulate the structure of data, as well as where and how data can flow through the organization?

Step 5: Find opportunities for target company data value creation.

Assess, understand, and determine the opportunities for marketing and operational improvements, cost reduction, untapped areas of growth, data monetization, cash flow improvement, and more.

  1. Which of the following advanced data and analytics use cases does the portfolio company have in place?
    • Customer acquisition
    • Marketing channel excellence
    • Working capital rationalization
    • Fixed asset deployment and maintenance
    • Operational labor transformation
    • Forecasting predictive analytics
    • Automated customer reporting
    • Supply chain optimization
  2. What use cases does the company conduct for data science predictive and prescriptive analytics?
  3. What is the target company’s data monetization strategy, and where are they with implementation?
  4. What is the company’s usage of big data to enhance marketing, sales, and customer service understanding and strategies?
  5. What third-party data does the company use to supplement internal data to drive enhanced insights into marketing and operating?

Conclusion

To accelerate data and analytics value creation for a portfolio company target, start the process during due diligence. Gaining tremendous insight into the potential for data will accelerate the plan once the deal is closed and allow for a running start on data analytics value creation. With these insights, the PE firm, in partnership with their portfolio company, will generate fast data ROI and enable financial and operational transformation, EBITDA growth, and enhanced cash flow.

At 2nd Watch, we help private equity firms implement comprehensive data analytics solutions from start to finish. Our data experts guide, oversee, and implement focused analytics projects to help clients attain more value from modern analytics. Contact us for a complimentary 90-minute whiteboard session to get started.

 

Jim Anfield, Principle and Health Care Practice Leader

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Data Strategy for Insurance: How to Build the Right Foundation for Analytics and Machine Learning

Analytics and machine learning technologies are revolutionizing the insurance industry. Rapid fraud detection, improved self service, better claims handling, and precise customer targeting are just some of the possibilities. Before you jump head first into an insurance analytics project, however, you need to take a step back and develop an enterprise data strategy for insurance that will ensure long-term success across the entire organization.

Want better dashboards? Our data and analytics insurance team is here to help. Learn more about our data visualization starter pack.

Data management Strategy for Insurance companies

Here are the basics to help get you started – and some pitfalls to avoid.

The Foundation of Data Strategy for Insurance

Identify Your Current State

What are your existing analytics capabilities? In our experience, data infrastructure and analysis are rarely implemented in a tidy, centralized way. Departments and individuals choose to implement their own storage and analytical programs, creating entire systems that exist off the radar. Evaluating the current state and creating a roadmap empowers you to conduct accurate gap analysis and arrange for all data sources to funnel into your final analytics tool.

Define Your Future State

A strong ROI depends on a clear and defined goal from the start. For insurance analytics, that means understanding the type of analytics capabilities you need (e.g., real-time analytics, predictive analytics) and the progress you want to make (e.g., more accurate premiums, reduced waste, more personalized policies). Through stakeholder interviews and business requirements, you can determine the exact fix to reduce waste during the implementation process.

Pitfalls to Avoid

Even with a solid roadmap, some common mistakes can hinder the end result of your insurance analytics project. Keep these in mind during the planning and implementation phases.

Don’t Try to Eat the Elephant in One Bite

Investing $5 million in an all-encompassing enterprise-wide platform is good in theory. However, that’s a hefty price tag for an untested concept. We recommend our clients start on a more strategic proof of concept that can provide ROI in months rather than years.

Maximize Your Data Quality

Your insights are only as good as your data. Even with a well-constructed data hub, your findings cannot turn low-quality data into gems. Data quality management within your business provides a framework for better outcomes by identifying old or unreliable data. But your team needs to take it to the next level, acting with care to input accurate and timely data that your internal system can use for analysis.

Align Analytics with Your Strategic Goals

Alignment with your strategic goals is a must for any insurance analytics project. There needs to be consensus among all necessary stakeholders – business divisions, IT, and top business executives – or each group will pull the project in different directions. This challenge is avoidable if the right stakeholders and users are included in planning the future state of your analytics program.

Integrate Analytics with Your Whole Business

Incompatible systems result in significant waste in any organization. If an analytics system cannot access the range of data sources it needs to evaluate, then your findings will fall short. During one project, our client wanted to launch a claims system and assumed it would be a simple integration of a few systems. When we conducted our audit, we found that 25 disparate source systems existed. Taking the time up front to run these types of audits prevents headaches down the road when you can’t analyze a key component of a given problem.

If you have any questions or are looking for additional guidance on analytics, machine learning, or data strategy for insurance, 2nd Watch’s insurance data and analytics team is happy to help. Feel free to contact us here.

Data strategy insurance industry

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Manufacturing Analytics: The Power of Data in the Manufacturing Industry

The effects of the pandemic have hit the manufacturing industry in ways no one could have predicted. During the last 18 months, a new term has come up frequently in the news and in conversation: the supply chain crisis. Manufacturers have been disrupted in almost every facet of their business, and they have been put to the test as to whether they can weather these challenges or not. 

Manufacturing Analytics: The Power of Data in the Manufacturing Industry

 

Manufacturing businesses that began a digital transformation prior to the current global crisis have been more agile in handling the disruptions. That is because manufacturers using data analytics and cloud technology can be flexible in adopting the capabilities they need for important business goals, be able to identify inefficiencies more quickly and be equipped to adopt a hybrid workforce to make sure production doesn’t stall. 

The pandemic has identified and accelerated the need for manufacturers to digitize and harness the power of modern technology. Real-time data and analytics are fundamental to the manufacturing industry because they create the contextual awareness that is crucial for optimizing products and processes. This is especially important during the supply chain crisis, but this goes beyond the scope of the pandemic. Manufacturers will want to, despite the external circumstances, automate for quicker and smarter decisions in order to remain competitive and have a positive impact on the bottom line. 

In this article, we’ll identify the use cases and benefits of manufacturing analytics, which can be applied in any situation at any time. 

What is Manufacturing Analytics?

Manufacturing analytics is used to capture, process, and analyze machine, operational, and system data in order to manage and optimize production. It is used in critical functions – such as planning, quality, and maintenance – because it has the ability to predict future use, avoid failures, forecast maintenance requirements, and identify other areas for improvement. 

To improve efficiency and remain competitive in today’s market, manufacturing companies need to undergo a digital transformation to change the way their data is collected. Traditionally, manufacturers capture data in a fragmented manner: their staff manually check and record factors, fill forms, and note operation and maintenance histories for machines on the floor. These practices are susceptible to human error, and as a result, risk being highly inaccurate. Moreover, these manual processes are extremely time-consuming and open to biases. 

Manufacturing analytics solves these common issues. It collects data from connected devices, which reduces the need for manual data collection and, thereby, cuts down the labor associated with traditional documentation tasks. Additionally, its computational power removes the potential errors and biases that traditional methods are prone to. 

Because manufacturing equipment collects massive volumes of data via sensors and edge devices, the most efficient and effective way to process this data is to feed the data to a cloud-based manufacturing analytics platform. Without the power of cloud computing, manufacturers are generating huge amounts of data, but losing out on potential intelligence they have gathered. 

Cloud-based services provide a significant opportunity for manufacturers to maximize their data collection. The cloud provides manufacturers access to more affordable computational power and more advanced analytics. This enables manufacturing organizations to gather information from multiple sources, utilize machine learning models, and ultimately discover new methods to optimize their processes from beginning to end. 

Additionally, manufacturing analytics uses advanced models and algorithms to generate insights that are near-real-time and much more actionable. Manufacturing analytics powered by automated machine data collection unlocks powerful use cases for manufacturers that range from monitoring and diagnosis to predictive maintenance and process automation. 

Use Cases for Cloud-Based Manufacturing Analytics

The ultimate goal of cloud-based analytics is to transition from having descriptive to predictive practices. Rather than just simply collecting data, manufacturers want to be able to leverage their data in near-real-time to get ahead of issues with equipment and processes and to reduce costs. Below are some business use cases for automated manufacturing analytics and how they help enterprises achieve predictive power:

Demand Forecasting and Inventory Management

Manufacturers need to have complete control of their supply chain in order to better manage inventory. However, demand planning is complex. Manufacturing analytics makes this process simpler by providing near-real-time floor data to support supply chain control, which leads to improved purchase management, inventory control, and transportation. The data provides insight into the time and costs needed to build parts and run a given job, which gives manufacturers the power to more accurately estimate their needs for material to improve planning. 

Managing Supply Chains

For end-to-end visibility in the supply chain, data can be captured from materials in transit and sent straight from external vendor equipment to the manufacturing analytics platform. Manufacturers can then manage their supply chains from a central hub of data collection that organizes and distributes the data to all stakeholders. This enables manufacturing companies to direct and redirect resources to speed up or down. 

Price Optimization

In order to optimize pricing strategies and create accurate cost models, manufacturers need exact timelines and costs. Having an advanced manufacturing analytics platform can help manufacturers determine accurate cycle times to ensure prices are appropriately set. 

Product Development

To remain competitive, manufacturing organizations must invest in research and development (R&D) to build new product lines, improve existing models, and introduce new services. Manufacturing analytics makes it possible for this process to be simulated, rather than using traditional iterative modeling. This reduces R&D costs greatly because real-life conditions can be replicated virtually to predict performance. 

Robotization

Manufacturers are relying more on robotics. As these robots become more intelligent and independent, the data they collect while they execute their duties will increase. This valuable data can be used within a cloud-based manufacturing analytics platform to really control quality at the micro-level. 

Computer Vision Applications

Modern automated quality control harnesses advanced optical devices. These devices can collect information via temperature, optics, and other advanced vision applications (like thermal detection) to precisely control stops.

Fault Prediction and Preventative Maintenance

Using near-real-time data, manufacturers can predict the likelihood of a breakdown – and when it may happen – with confidence. This is much more effective than traditional preventive maintenance programs that are use-based or time-based. Manufacturing analytics’s accuracy to predict when and how a machine will break down allows technicians to perform optimal repairs that reduce overall downtime and increase productivity. 

Warranty Analysis

It’s important to analyze information from failed products to understand how products are withstanding the test of time. With manufacturing analytics, products can be improved or changed to reduce failure and therefore costs. Collecting warranty data can also shed light on the use (and misuse) of products, increase product safety, improve repair procedures, reduce repair times, and improve warranty service. 

Benefits of Manufacturing Analytics

In short, cloud-based manufacturing analytics provides awareness and learnings on a near-real-time basis. For manufacturers to be competitive, contextual awareness is crucial for optimizing product development, quality, and costs. Production equipment generates huge volumes of data, and manufacturing analytics allows manufacturers to leverage this data stream to improve productivity and profitability. Here are the tangible benefits and results of implementing manufacturing analytics:

Full Transparency and Understanding of the Supply Chain

In today’s environment, owning the supply chain has never been more critical. Data analytics can help mitigate the challenges that have cropped up with the current supply chain crisis. For manufacturing businesses, this means having the right number of resources. Data analytics allows manufacturers to remain as lean as possible, which is especially important in today’s global climate. Organizations need to use data analytics to ensure they have the right amount of material and optimize their supply chains during a time when resources are scarce and things are uncertain. 

Reduced Costs

Manufacturing analytics reveals insights that can be used to optimize processes, which leads to cost savings. Predictive maintenance programs decrease downtime and manage parts inventories more intelligently, limiting costs and increasing productivity. Robotics and machine learning reduce labor and the associated costs. 

Increased Revenue

Manufacturers must be dynamic in responding to demand fluctuations. Near-real-time manufacturing analytics allows companies to be responsive to ever-changing demands. At any given time, manufacturing companies have up-to-date insights into inventory, product, and supply chains, allowing them to adjust to demand accordingly in order to maintain delivery times. 

Improved Efficiency Across the Board

The amount of information that product equipment collects enables manufacturers to increase efficiency in a variety of ways. This includes reducing energy consumption, mitigating compliance errors, and controlling the supply chain. 

Greater Customer Satisfaction

At the end of the day, it is important to know what customers want. Data analytics is a crucial tool in collecting data from customer feedback, which can be applied to streamlining the process per the customer’s requirements. Manufacturers can analyze the data collected to determine how to personalize services for their consumers, thereby, increasing customer satisfaction. 

Conclusion

The effects of COVID-19 have shaken up the manufacturing industry. Because of the pandemic’s disruptions, manufacturers are realizing the importance of robust tools – like cloud computing and data analytics – to remain agile, lean, and flexible regardless of external challenges. The benefits that organizations can reap from these technologies go far beyond the horizon of the current supply chain crisis. Leading manufacturers are using data from systems across the organization to increase efficiency, drive innovation, and improve overall performance in any environment.

2nd Watch’s experience managing and optimizing data means we understand industry-specific data and systems. Our manufacturing data analytics solutions and consultants can assist you in building and implementing a strategy that will help your organization modernize, innovate, and outperform the competition. Learn more about our manufacturing solutions and how we can help you gain deep insight into your manufacturing data!

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What Is Looker and Why Might You Need It?

Maybe you’re venturing into data visualization for the first time, or maybe you’re interested in how a different tool could better serve your business. Either way, you’re likely wondering, “What is Looker?” and, “Could it be right for us?” In this blog post, we’ll go over the benefits of Looker, how it compares to Power BI and Tableau, when you may want to use Looker, and how to get started if you decide it’s the right tool for your organization.

What Is Looker and Why Might You Need It?

What is Looker?

Looker is a powerful business intelligence (BI) tool that can help a business develop insightful visualizations. It offers a user-friendly workflow, is completely browser-based (eliminating the need for desktop software), and facilitates dashboard collaboration. Among other benefits, users can create interactive and dynamic dashboards, schedule and automate the distribution of reports, set custom parameters to receive alerts, and utilize embedded analytics.

How is Looker different?

We can’t fully answer “What is Looker?” without seeing how it stacks up against competitors:

How is Looker different?

Does Looker fit into my analytics ecosystem?

When to Use Looker

If you’re looking for customized visuals, collaborative dashboards, and a single source of truth, plus top-of-the-line customer support, Looker might be the best BI platform for you. Being fully browser-based cuts down on potential confusion as your team gets up and running, and pricing customized to your company means you get exactly what you need to meet your company’s analytics goals.

When Not to Use Looker

If you’ve already bought into the Microsoft ecosystem, Power BI is your best bet. Introducing another tool will likely only create confusion and increase costs.

When someone says “Tableau,” the first thing that comes to mind is how impressive the visuals are. If you want the most elegant visuals and a platform that’s intuitive for analysts and business users alike, you may want to go with Tableau.

How do I get started using Looker?

You can get started using Looker in four basic steps:

1. Determine if your data is analytics-ready.

Conduct an audit of where your data is stored, what formats are used, etc. You may want to consider a data strategy project before moving forward with a BI platform implementation.

2. Understand your company’s BI needs and use cases.

Partner with key stakeholders across the business to learn how they currently use analytics and how they hope to use more advanced analytics in the future. What features do they or their staff need in a BI tool?

3. Review compliance and data governance concerns.

When in conversation with those key stakeholders, discuss their compliance and data governance concerns as well. Bring your technology leaders into the discussion to get their valuable perspectives. You should have an enterprise-wide stance on these topics that informs any additions to your tech stack.

4. Partner with a trusted resource to ensure a smooth implementation.

Our consultants’ hands-on experience with Looker can contribute to a faster, simpler transition. Plus, 2nd Watch can transfer the necessary knowledge to make sure your team is equipped to make the most of your new BI tool. We can even help with the three previous steps, guiding the process from start to finish.

If you still have questions about if Looker is worth considering for your organization, or if you’re ready to get started with Looker, contact us here.

Partner with a trusted resource to ensure a smooth implementation

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3 Questions to Help You Build Your Analytics Roadmap

In our experience, many analytics projects have the right intentions such as:

  • A more holistic view of the organization
  • More informed decision making
  • Better operational and financial insights

With incredible BI and analytics tools such as Looker, Power BI, and Tableau on the market, it’s tempting to start by selecting a tool believing it to be a silver bullet. While these tools are all excellent choices when it comes to visualization and analysis, the road to successful analytics starts well before tool selection.

So where do you begin? By asking and answering a variety of questions for your organization, and building a data analytics roadmap from the responses. From years of experience, we’ve seen that this process (part gap analysis, part soul-searching) is non-negotiable for any rewarding analytics project.

Building an Advanced Data Analytics Roadmap

Give the following questions careful consideration as you run your current state assessment:

How Can Analytics Support Your Business Goals?

There’s a tendency for some stakeholders not immersed in the data to see analytics as a background process disconnected from the day to day. That mindset is definitely to their disadvantage. When businesses fixate on analytical tools without a practical application, they put the cart before the horse and end up nowhere fast. Yet when analytics solutions are purposeful and align with key goals, insights appear faster and with greater results.

One of our higher education clients is a perfect example. Their goal? To determine which of their marketing tactics were successful in converting qualified prospects into enrolled students. Under the umbrella of that goal, their stakeholders would need to answer a variety of questions:

  • How long was the enrollment process?
  • How many touchpoints had enrolled students encountered during enrollment?
  • Which marketing solutions were the most cost effective at attracting students?

As we evaluated their systems, we recognized data from over 90 source systems would be essential to provide the actionable insight our client wanted. By creating a single source of truth that fed into Tableau dashboards, their marketing team was able to analyze their recruiting pipeline to determine the strategies and campaigns that worked best to draw new registrants into the student body.

This approach transcends industries. Every data analytics roadmap should reflect on and evaluate the most essential business goals. More than just choosing an urgent need or reacting to a surface level problem, this reevaluation should include serious soul-searching.

The first goals you decide to support should always be as essential to you as your own organizational DNA. When you use analytics solutions to reinforce the very foundation of your business, you’ll always get a higher level of results. With a strong use case in hand, you can turn your analytics project into a stepping stone for bigger and better things.

What Is Your Analytical Maturity?

You’re not going to scale Mt. Everest without the gear and training to handle the unforgiving high altitudes, and your organization won’t reach certain levels of analytical sophistication without hitting the right milestones first. Expecting more than you’re capable of out of an analytics project is a surefire path to self-sabotage. That’s why building a data analytics roadmap always requires an assessment of your data maturity first.

However, there isn’t a single KPI showing your analytical maturity. Rather, there’s a combination of factors such as the sophistication of your data structure, the thoroughness of your data governance, and the dedication of your people to a data-driven culture.

Here’s what your organization can achieve at different levels of data maturity:

  • Descriptive Analytics – This level of analytics tells you what’s happened in the past. Typically, organizations in this state rely on a single source system without the ability to cross-compare different sources for deeper insight. If there’s data quality, it’s often sporadic and not aligned with the big picture.
  • Diagnostic Analytics – Organizations at this level are able to identify why things happened. At a minimum, several data sets are connected, allowing organizations to measure the correlation between different factors. Users understand some of the immediate goals of the organization and trust the quality of data enough to run them through reporting tools or dashboards.
  • Predictive Analytics – At this level, organizations can anticipate what’s going to happen. For starters, they need large amounts of data – from internal and external sources – consolidated into a data lake or data warehouse. High data governance standards are essential to establish consistency and accuracy in analytical insight. Plus, organizations need to have complex predictive models and even machine learning programs in place to make reliable forecasts.
  • Prescriptive Analytics – Organizations at the level of prescriptive analytics are able to use their data to not only anticipate market trends and changing behaviors but act in ways that maximize outcomes. From end to end, data drives decisions and actions. Moreover, organizations have several layers of analytics solutions to address a variety of different issues.

What’s important to acknowledge is that each level of analytics is a sequential progression. You cannot move up in sophistication without giving proper attention to the prerequisite data structures, data quality, and data-driven mindsets.

For example, if an auto manufacturer wants to reduce their maintenance costs by using predictive analytics, there are several steps they need to take in advance:

  • Creating a steady feed of real-time data through a full array of monitoring sensors
  • Funneling data into centralized storage systems for swift and simple analysis
  • Implementing predictive algorithms that can be taught or learn optimal maintenance plans or schedules

Then, they can start to anticipate equipment failure, forecast demand, and improve KPIs for workforce management. Yet no matter your industry, the gap analysis between the current state of your data maturity and your goals is essential to designing a roadmap that can get you to your destinations fastest.

What’s the State of our Data?

Unfortunately for any data analytics roadmap, most organizations didn’t grow their data architecture in a methodical or intentional way. Honestly, it’s very difficult to do so. Acquisitions, departmental growth spurts, decentralized operations, and rogue implementations often result in an over-complicated web of data.

When it comes to data analysis, simple structures are always better. By mapping out the complete picture and current state of your data architecture, your organization can determine the best way to simplify and streamline your systems. This is essential for you to obtain a complete perspective from your data.

Building a single source of truth out of a messy blend of data sets was essential for one of our CPG clients to grow and lock down customers in their target markets. The modern data platform we created for their team consolidated their insight into one central structure, enabling them to track sales and marketing performance across various channels in order to help adjust their strategy and expectations. Centralized data sources offer a springboard into data science capabilities that can help them predict future sales trends and consumer behaviors – and even advise them on what to do next.

Are you building a data analytics roadmap and are unsure of what your current analytics are lacking? 2nd Watch can streamline your search for the right analytics fit. 

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