How Top Logistics Companies Use Data to Outperform the Competition

Technology, and more importantly, the proper understanding of data, is catapulting one of America’s oldest industries forward, and it’s just getting started. Traditionally, logistics companies operated based on relationships and trust between customers and carriers that culminated over years and years of working together. While these elements will always be important, logistics companies that want to stay competitive know if you’re not automating, you’re falling behind.

Over the last few years, we’ve seen a huge shift in the industry as everyone is fervently implementing and relying on technology to advance and secure their place in the extremely competitive logistics space. What are best-in-class logistics companies using to stay ahead of the game? Data.

Using Logistics Company Data for Fast Decision-Making

Logistics is a fast-paced industry. Spend too much time making a decision and the opportunity is gone. Act hastily and make the wrong decision, and you’ve just lost out on profits. With stakes this high, there is no room for uncertainty. Everyone, from customer and carrier reps to the CEO, has the same thing on their mind: maximizing profit.

In the case of sales, you may be successful today with a few top reps who have mastered their craft over time, know the ins and outs of the logistics industry, and have built strong relationships with customers. What about those reps just getting started, though? To differentiate yourself and stay competitive, it’s important to arm every rep, on all pegs of the totem pole, with information they need to make fast decisions.

Why waste time reaching out to multiple carriers to move a load if the data can automatically rank the best ones for the job? Utilizing logistics company data in this regard guarantees you can make the best decision in the least amount of time, ensuring your margins stay high and, more importantly, your customers stay happy. For most logistics companies, this information is already at your disposal in systems throughout the organization.

Forward-thinking CEOs know it’s their responsibility to take full advantage of the organization’s most valuable asset: its data. By prioritizing the centralization, organization, and accessibility of logistics company data across all departments, businesses can expect to start operating more optimally than ever before.

Want better logistics dashboards? Our modern data and analytics experts are here to help. Contact us for a dashboard UI/UX session.

Increasing Enterprise-Wide Data Use

On any given day, data is flying in from every direction. There are loads to track, carriers to call, and rates to analyze – and that’s just before breakfast. Sure, data is being generated internally, but what about all the data coming in from outside your organization? What’s the DAT looking like today? Are we more successful using third-party tools to book loads? Analyzing any of these factors incorrectly or, even worse, failing to analyze any of these at all can have detrimental effects on your bottom line over time. But let’s think positively. Let’s focus on what’s possible when you have a process in place to collect and analyze all this data. Are your reps securing better rates? Are you correcting bottlenecks found in the process?

Increasing the use of data is an effort that should span all departments, not just something that falls in IT’s lap. Shifting focus and becoming a data-driven logistics company has benefits that trickle throughout the entire organization. It may fall on IT to properly implement the solution, but the effort needs to be spearheaded by a united leadership team that understands that the real insights are hiding, buried deep in the data, and ready to be used and transformed into profits.

From a happy finance department that can finally accurately create an annual budget to a happy marketing team that’s able to identify the most effective platforms, your company will be amazed at how quickly the benefits of well-managed data impact your day-to-day business.


The 4-Step Plan to Optimize Your Data Analytics in Insurance

Data is one of the insurance industry’s greatest assets, which is why data analytics is so important. Before digital transformations swept the business world, underwriters and claims adjusters were the original data-driven decision makers, gathering information to assess a customer’s risk score or evaluate potential fraud. Algorithms have accelerated the speed and complexity of analytics in insurance, but some insurers have struggled to implement the framework necessary to keep their underwriting, fraud detection, and operations competitive.

The good news is that we have a clear road map for how to implement data analytics in insurance that garners the best ROI for your organization. Here are the four steps you need to unlock even more potential from your data.

4-Step Plan to Optimize Your Data Analytics in Insurance

Step 1: Let your business goals, not your data, define your strategy

As masters of data gathering, insurers have no shortage of valuable and illuminating data to analyze. Yet the abundance of complex data flowing into their organizations creates an equally vexing problem: conducting meaningful analysis rather than spur-of-the-moment reporting.

It’s all too easy for agents working on the front lines to allow the data flowing into their department to govern the direction of their reporting. Though ad hoc reporting can generate some insight, it rarely offers the deep, game-changing perspective businesses need to remain competitive.

Instead, your analytics strategy should align with your business goals if you want to yield the greatest ROI. Consider this scenario. A P&C insurer wants to increase the accuracy of their policy pricing in a way that retains customers without incurring additional expenses from undervalued risk. By using this goal to define their data strategy, it’s a matter of identifying the data necessary to complete that objective.

If, for example, they lack complex assessments of the potential risks in the immediate radius of a commercial property (e.g., a history of flood damage, tornado warnings, etc.), the insurer can seek out that data from an external source to complete the analysis, rather than restricting the scope of their analysis to what they have.

Step 2: Get a handle on all of your data

The insurance industry is rife with data silos. Numerous verticals, LoBs, and M&A activity have created a disorganized collection of platforms and data storage, often with their own incompatible source systems. In some cases, each unit or function has its own specialized data warehouse or activities that are not consistent or coordinated. This not only creates a barrier to cohesive data analysis but can result in a hidden stockpile of information as LoBs make rogue implementations off the radar of key decision-makers.

Before you can extract meaningful insights, your organization needs to establish a single source of truth, creating a unified view of your disparate data sources. One of our industry-leading insurance clients provides a perfect example of the benefits of data integration. The organization had grown over the years through numerous acquisitions, and each LoB brought their own unique policy and claims applications into the fold. This piecemeal growth created inconsistency across their comprehensive insight.

For example, the operational reports conducted by each LoB reported a different amount of paid losses on claims for the current year, calling into question their enterprise-wide decision-making process. As one of their established partners, 2nd Watch provided a solution. Our team conducted a current state assessment, interviewing a number of stakeholders to determine the questions each group wanted answered and the full spectrum of data sources that were essential to reporting.

We then built data pipelines (using SSIS for ETL and SQL Server) to integrate the 25 disparate sources we identified as crucial to our client’s business. We unified the meta-data, security, and governance practices across their organizations to provide a holistic view that also remained compliant with federal regulation. Now, their monthly P&L and operational reporting are simplified in a way that creates agreements across LoBs – and helps them make informed decisions.

Step 3: Create the perfect dashboard(s)

You’ve consolidated and standardized your data. You’ve aligned your analytics strategy with your goals. But can your business users quickly obtain meaning from your efforts? The large data sets analyzed by insurance organizations can be difficult to parse without a way to visualize trends and takeaways. For that very reason, building a customized dashboard is an essential part of the data analytics process.

Your insurance analytics dashboard is not a one-size-fits-all interface. Similarly, to how business goals should drive your strategy, they should also drive your dashboards. If you want people to derive quick insights from your data, the dashboard they’re using should evaluate KPIs and trends that are relevant to their specific roles and LoBs.

Claims adjusters might need a dashboard that compares policy type by frequency of utilization and cost, regional hotspots for claims submissions, or fraud priority scores for insurance fraud analytics. C-suite executives might be more concerned with revenue comparisons across LoBs, loss ratios per policy, and customer retention by vertical. All of those needs are valid. Each insurance dashboard should be designed and customized to satisfy the most common challenges of the target users in an interactive and low-effort way.

Much like the data integration process, you’ll find ideal use cases by conducting thorough stakeholder interviews. Before developers begin to build the interface, you should know the current analysis process of your end users, their pain points, and their KPIs. That way, you can encourage them to adopt the dashboards you create, running regular reports that maximize the ROI of your efforts.

Step 4: Prepare for ongoing change

A refined data strategy, consolidated data architecture, and intuitive dashboards are the foundation for robust data analytics in insurance. Yet the benchmark is always moving. There’s an unending stream of new data entering insurance organizations. Business goals are adjusting to better align with new regulations, global trends, and consumer needs. Insurers need their data analytics to remain as fluid and dynamic as their own organizations. That requires your business to have the answers to a number of questions.

How often should your dashboard update? Do you need real-time analytics to make up-to-the-minute assessments on premiums and policies? How can you translate the best practices from profitable use cases into different LoBs or roles? Though these questions (and many others) are not always intuitive, insurers can make the right preparations by working with a partner that understands their industry.

Here’s an example: One of our clients had a vision to implement a mobile application that enabled rideshare drivers to obtain commercial micro-policies based on the distance traveled and prevailing conditions. After we consolidated and standardized disparate data systems into a star schema data warehouse, we automated the ETL processes to simplify ongoing processes.

From there, we provided our client with guidance on how to build upon their existing real-time analytics to deepen the understanding of their data and explore cutting-edge analytical solutions. Creating this essential groundwork has enabled our team to direct them as we expand big data analytics capabilities throughout the organization, implementing a roadmap that yields greater effectiveness across their analytics.

Are you looking for more help to optimize your insurance data analytics? Get in touch to schedule a complimentary whiteboarding session with 2nd Watch experts.


Healthcare Big Data Use Cases and Benefits

Improving Care Quality, Reducing Nurse Turnover, and More

Hospital patient satisfaction (HCAHPS scores) and nurse turnover are two big issues hospitals are currently facing. Both issues have major implications with regard to patient outcomes and hospital efficiency and profitability. For example: On top of an already-high nurse turnover rate of 19.1%, the US healthcare system is facing an alarming rate of nurse retirements by the end of 2022. The cost of turnover is also incredibly high – it is estimated that each percentage point of nurse turnover costs an average hospital almost $350,000 in terms of recruiting, onboarding, and training. And of course, high nurse turnover has a negative impact on patient care.

Healthcare Big Data

To reduce the rate of nurse turnover, care facilities and hospitals must understand the causes of turnover, address those issues, and track their progress to adjust as needed. Ideally, hospitals would survey patients and nurses to receive feedback directly. However, response rates are typically very low. The alternative? Harness the power of healthcare big data. Healthcare big data can be used in a variety of use cases with several key benefits:

Big Data Use Cases in Healthcare

  • Capture publicly available data across social media and online review sites to aggregate sentiment and find patterns.
  • Understand the root causes of various issues, including such issues as HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) quality scores, nurse turnover, and patient satisfaction.
  • Benchmark your results against national data for similar organizations.
  • Forecast how changes in the healthcare industry will affect sentiment in the future.

Benefits of Big Data in Healthcare

  • Improve patient care quality.
  • Reduce nurse turnover rates.
  • Increase patient acquisition.
  • Ultimately, help improve patient care and healthcare outcomes.

There’s a wealth of information on social media and online review sites like Glassdoor, Yelp, Google reviews, Becker’s, Facebook, and WebMD. We investigated how a Chicago-area hospital could use this publicly available data in conjunction with HCAHPS survey results and nurse satisfaction surveys to address the above healthcare big data use cases and reap the benefits.

Download the white paper to learn about our findings.


3 Options for Getting Started with a Modern Data Warehouse

In previous blog posts, we laid out the benefits of a modern data warehouse, explored the different types of modern data warehouses available, and discussed where a modern data warehouse fits in your overall data architecture.

Download Now: Modern Data Warehouse Comparison Guide [Snowflake, Amazon Redshift, Azure Synapse, and Google BigQuery]

There is no such thing as a one-size-fits-all data warehouse. To that end, there is no singular approach to getting started. Getting started depends on your goals, needs, and where you are today. In this blog post, we’ll outline a few options 2nd Watch offers for getting started with a modern data warehouse and the details for each.

  • Option 1: Data Architecture Whiteboard Session
  • Option 2: Modern Data Warehouse Strategy Session
  • Option 3: Modern Data Warehouse Quickstart

Option 1: 60-Minute Data Architecture Assessment

A 60-minute data architecture assessment is a great option to see how a modern data warehouse would fit in your current environment and what would be involved to get from where you are now to where you want to be.

During this session, we will outline a plan to achieve your goals and help you understand the tools, technologies, timeline, and cost to get there.

Who is this for? Organizations in the very early planning stages

Duration: 60 minutes

More Information

Option 2: Modern Data Warehouse Strategy

In order to see ROI and business value from your modern data warehouse, you must have a clear plan on how you are going to use it. During a modern data warehouse strategy project, our team will work with your stakeholders to understand your business goals and design a tech strategy to ensure business value and ROI from your data environment.

Who is this for? Organizations in the early planning stages looking to establish the business use case, cost benefits, and ROI of a modern data warehouse before getting started

Duration: 2-, 4-, 6-, or 8-week strategies are available

More Information

Option 3: Modern Data Platform Quickstart

You have your strategy laid out and are ready to get started ASAP. The modern data platform quickstart is a great option to get your modern data warehouse up and running in as few as six weeks.

During this quickstart, we’ll create a scalable data warehouse; clean, normalize, and ingest data; and even provide reports for predefined use cases.

Who is this for? Organizations that have outlined their strategy and are ready to start seeing the benefits of a modern data warehouse

Duration: 6 weeks

More Information

Not sure where to begin? We recommend beginning with a 60-minute data architecture assessment. This session allows us to walk through your current architecture, understand your organization’s pain points and goals for analytics, brainstorm on a future state architecture based on your goals, and then come up with next steps. Furthermore, the assessment allows us to determine if your organization needs to make a change, what those changes are, and how you might go about implementing them. Simply put, we want to understand the current state, learn about the future state of what you want to build toward, and help you create a plan so you can successfully execute on a modern data warehouse project.

A Word of Warning

Modern data warehouses are a big step forward from traditional on-premise architectures. They allow organizations to innovate quicker and provide value to the business much faster. An organization has many options in the cloud and many vendors offer a cloud data warehouse, but be careful: building a modern data warehouse architecture is highly involved and may require multiple technologies to get you to the finish line.

The most important thing to do when embarking on a modern data warehouse initiative is to have an experienced partner to guide you through the process the right way from establishing why a cloud data warehouse is important to your organization to outlining what the future state vision should be to develop a plan to get you there.

Data warehouse architecture is changing, don’t fall behind your competition! With multiple options for getting started, there is no reason to wait.

We hope you found this information valuable. If you have any questions or would like to learn more, please contact us and we’ll schedule a time to connect.


4 Issues in Data Migration from Legacy Systems to Avoid

The scales have finally tipped! According to a Flexera survey, 93% of organizations have a multi-cloud strategy and 53% are now operating with advanced cloud maturity. For those who are now behind the bell curve, it’s a reminder that keeping your data architecture in an on-premises solution is detrimental to remaining competitive. On-prem architecture restricts your performance and the overall growth and complexity of your analytics. Here are some of the setbacks of remaining on-prem and the benefits of data migration from legacy systems.

Looking for the right path to data modernization? Learn about our 60-minute data architecture assessment and how it will get you there.

4 Issues in Data Migration from Legacy Systems to Avoid

Greater Decentralization

For most organizations, data architecture did not grow out of an intentional process. Many on-prem storage systems developed from a variety of events ranging from M&A activity and business expansion to vertical-specific database initiatives and rogue implementations. As a result, they’re often riddled with data silos that prevent comprehensive analysis from a single source of truth.

When organizations conduct reporting or analysis with these limitations, they are at best only able to find out what happened – not predict what will happen or narrow down what they should do. The predictive analytics and prescriptive analytics that organizations with high analytical maturity are able to conduct are only possible if there’s a consolidated and comprehensive data architecture.

Though you can create a single source of data with an on-prem setup, a cloud-based data storage platform is more likely to prevent future silos. When authorized users can access all of the data from a centralized cloud hub, either through a specific access layer or the whole repository, they are less likely to create offshoot data implementations.

Slower Query Performance

The insights from analytics are only useful if they are timely. Some reports are evergreen, so a few hours, days, or even a week doesn’t alter the actionability of the insight all that much. On the other hand, real-time analytics or streaming analytics requires the ability to process high-volume data at low latency, a difficult feat for on-prem data architecture to achieve without enterprise-level funding. Even mid-sized businesses are unable to justify the expense – even though they need the insight available through streaming analysis to keep from falling behind larger industry competitors.

Using cloud-based data architecture enables organizations to access much faster querying. The scalability of these resources allows organizations of all sizes to ask questions and receive answers at a faster rate, regardless of whether it’s real-time or a little less urgent.

Plus, those organizations that end up working with a data migration services partner can even take advantage of solution accelerators developed through proven methods and experience. Experienced partners are better at avoiding unnecessary pipeline or dashboard inefficiencies since they’ve developed effective frameworks for implementing these types of solutions.

More Expensive Server Costs

On-prem data architecture is far more expensive than cloud-based data solutions of equal capacity. When you opt for on-prem, you always need to prepare and pay for the maximum capacity. Even if the majority of your users are conducting nothing more complicated than sales or expense reporting, your organization still needs the storage and computational power to handle data science opportunities as they arise.

All of that unused server capacity is expensive to implement and maintain when the full payoff isn’t continually realized. Also, on-prem data architecture requires ongoing updates, maintenance, and integration to ensure that analytics programs will function to the fullest when they are initiated.

Cloud-based data architecture is far more scalable, and providers only charge you for the capacity you use during a given cycle. Plus, it’s their responsibility to optimize the performance of your data pipeline and data storage architecture – letting you reap the full benefits without all of the domain expertise and effort.

Hindered Business Continuity

There’s a renewed focus on business continuity. The recent pandemic has illuminated the actual level of continuity preparedness worldwide. Of the organizations that were ready to respond to equipment failure or damage to their physical buildings, few were ready to have their entire workforce telecommuting. Those with their data architecture already situated in the cloud fared much better and more seamlessly transitioned to conducting analytics remotely.

The aforementioned accessibility of cloud-based solutions gives organizations a greater advantage over traditional on-prem data architecture. There is limited latency when organizations need to adapt to property damage, natural disasters, pandemic outbreaks, or other watershed events. Plus, the centralized nature of this type of data analytics architecture prevents unplanned losses that might occur if data is stored in disparate systems on-site. Resiliency is at the heart of cloud-based analytics.

It’s time to embrace data migration from legacy systems in your business. 2nd Watch can help! We’re experienced with migration legacy implementations to Azure Data Factory and other cloud-based solutions.

Let’s Start Your Data Migration


Software and Solutions for Marketers

Software & Solutions for Marketers is the final installment in our Marketers’ Guide to Data Management and Analytics series. Throughout this series, we’ve covered 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, part two here, and part three here.

Software and Solutions for Marketers

In this last section, we will cover various aspects of software and solutions for marketing, including:

  • The differences between the cloud and on-premise (on-prem) solutions
  • Customer data platforms (CDP)
  • Custom development (custom dev)

Cloud vs. On-Prem


Also known as “cloud computing,” the cloud is a global network of software and services that run over the internet on someone else’s server, as opposed to running locally on your computer or server.

Why It Matters for Marketers:

  • Get the flexibility your business needs. Today’s marketing teams are mobile, require a variety of working schedules, and are often spread across geographies and time zones. Cloud-based software and services are accessible by any device with an internet connection, quick to set up, and reliable to access, regardless of the user’s location or device.
  • Deliver the level of service your customers expect. Hosting your website or e-commerce business on the cloud means your site won’t get bogged down with high traffic or large data files. Additionally, hosting your data in the cloud reduces the amount of siloed information, empowering teams to work more seamlessly and deliver a higher quality, more personalized experience to customers.
  • Spend your money on campaigns, not infrastructure. While many softwares are sold with on-premise or cloud options, the cloud-native options (tools such as Snowflake, Azure, AWS, and Looker) enable marketers to use these technologies with little to no reliance on IT resources to maintain the back-end infrastructure.

Real-World Examples:

Most marketing organizations use cloud-based applications such as Salesforce, HubSpot, or Sprout Social. These cloud-based applications allow marketing users to quickly and reliably create, collaborate on, and manage their marketing initiatives without being tied to a single location or computer.


On-premise or on-prem refers to any software, storage, or service run from on-site computers or servers.

Why It Matters for Marketers:

Most marketing software is run on the cloud these days. Cloud solutions are faster, more dynamic, and more reliable.

So why would a business choose on-prem? Today, there are two main reasons a business might still have on-prem software:

  1. The company is in a highly regulated industry where data ownership or security are big concerns.
  2. The company has legacy on-prem solutions with massive amounts of data, making the switch to cloud more challenging.

However, many of these companies still recognize the need to update their infrastructures. On-prem is harder to maintain and has reduced up-time as glitches or breaks are fixed at the speed of IT teams. What’s more: on-prem solutions can bottleneck your insights and ability to deliver insights at scale.

With this in mind, even companies with more complicated situations can use a hybrid of cloud and on-prem solutions. By doing this, they migrate less sensitive information to the cloud while keeping more regulated files on their own servers.

Real-World Examples:

In marketing, it’s likely that most data will be in the cloud but if you’re working with a client in a highly regulated industry, like government or healthcare, you might have some on-premise data sources.

Healthcare companies have patient privacy regulations like HIPAA about how customer data can be used, including marketing campaigns. In this case, an on-prem solution might be a better alternative to protect patients’ rights.

Customer Data Platform (CDP)

A customer data platform is a software solution that synthesizes customer data from various sources to keep them in sync with each other. CDPs often additionally offer the ability to send this data to a database of your choice for analytics.

Why It Matters for Marketers:

CDPs allow your various tools (such as your CRM, Google Analytics, and e-commerce systems) to stay in sync with each other around customer data. This means if you change a detail about a customer in one system, everyone else sees this update come through automatically without any manual updating.

Real-World Examples:

CDPs make it really easy to create quality account-based marketing (ABM) campaigns. CDPs deliver a persistent, accurate, and unified customer base, making it easy to use data throughout the ABM campaign.

For example, selecting and validating target accounts uses data from across your entire organization. Once pulled into the CDP, you can perform analytics on that data to identify the best accounts to go after. You will have thousands of attributes to better understand which customers are more likely to purchase.

One note: CDPs do not usually tie these customers and their information to other subject areas like products, orders, loyalty, etc. They are also not meant for analytic use cases. If you are doing deeper, company-wide analysis, you might want a data warehouse.

Custom Dev

Custom development, or custom dev, is a term that refers to any application or solution developed to satisfy the requirements of a specific user or business rather than for general use.

Why It Matters for Marketers:

Even the best out-of-the-box software or solutions are designed to overcome the challenges of a broad user base, providing functionality that only satisfies generalized needs. Custom dev solutions address your specific business needs in a way that gives you a competitive advantage or reduces the amount of time spent trying to make a generic software match your unique needs.

Real-World Examples:

One retail company was receiving flat files from a monthly vendor report that were hard to integrate with the rest of their reports. This made it challenging to get the deeper insights their marketing team needed to make informed omni-channel decisions.

As there were no tools available in the market with a connector to their system, a custom dev solution was needed. An application was created to automatically take in these flat files from the vendor so the marketing team could receive new data without the lengthy request and ingest process that relied heavily on IT resources. This enabled the marketing team to easily target the same customer across channels by using personalized campaigns that aligned with purchasing habits and history.

Another example of custom dev is the implementation of automated customer touchpoints. Adding features that trigger events based on business rules is a great way to personalize your customers’ experience. For example, you could create a rule that emails customers a coupon for their most frequently purchased product when they haven’t made a purchase in the past six months.

Throughout this Marketers’ Guide to Data Management and Analytics series, we hope you’ve learned about the different tools to manage, integrate, analyze, and use your data more strategically to get the most out of your investments. Please contact us to learn how we can help build and implement these various solutions, so you can better understand your customer base and target your customers accurately.