The 3 Stages of Data Analytics: How Data Transforms Business From the Ground Up

There is an immense amount of opportunity to be unlocked in your business by data. Everywhere we turn, new tools and innovations are hitting the market promising deeper insights, faster processes and smarter decision-making.

We also have more data to work with than ever before. We can collect data from anywhere, during any interaction. This allows us to reach unprecedented levels of customization and analysis, transforming the way we serve customers and create strategy. 

In 2019, people generate billions of data points in the form of emails, messages, tweets, searches, and Facebook posts every single day. Our cars alone generate 4 Terabytes of data on a daily basis, and connected machines, mobile devices, and IoT applications add to the deluge. Companies use this data to customize marketing efforts, automate processes, predict behavior, track supply chain activity, and more. 

But that’s not the whole picture. Reports, information, and insights are impressive, but they’re just a means to an end.

At Worthwhile, our vision for data analytics technology is to deliver the right data to the right people at the right time in order to drive business decisions. It’s about automating activity and action in your business so that you can not only deliver information, but also outline next steps based on the data. 

That’s the holy grail. 

But few companies are there yet.

Data Is a Big Deal

This summer, I had the opportunity to speak at IT Alliance’s Internal Tech Leaders Summer Meeting. In my session, I shared what we at Worthwhile see as the ultimate goal of data analytics and opportunities available and how that can impact your business. The bottom line is this: data is a big deal.

The opportunities for business information through data analytics are breathtaking. Here are just a few of the ways you can use data to drive business value:

Data Opportunities for Business Impact

From connected cars to AI-enabled toothbrushes (yes, really!), data holds enormous potential. But, in the infamous words of Peter Parker, with great power comes great responsibility. 

We all feel a bit squeamish when we learn, for example, that Facebook has been storing millions of passwords in plain text since 2012. Or that deepfake technology can place people in videos that aren’t real, doing things that they haven’t really done. 

Data misuse is an important concern, especially in light of how closely integrated technology has become with our businesses and our personal lives. The question is, how can companies responsibly use the data they collect to make positive impacts?

The Three Stages of Data Analytics

We use data in a multitude of ways to support various business functions. Those efforts can generally be divided into three categories: 

Big Data

Lots of data from lots of sources in real-time as much as possible (internal/external + structured/unstructured).

Goal: Determining where the answer is located/stored

Data Science

More advanced use of mathematics, statistics, and the process of modeling and inferring meaning from data. Emphasizes experimentation using tools like Machine Learning and Artificial Intelligence.

Goal: Finding the right question through exploration

Data Analytics

Analysis and reporting of known metrics in data to enable the delivery of the right data to the right people at the right time in order to drive business decisions.

Goal: Answering questions through a focused process and automating the response

We can use each of these endeavors to meet specific business needs, but much of what we can accomplish will be governed by where we fall on the Data Analytics Maturity Continuum. This continuum involves three stages:


Analytics Growth Continuum

Every business exists somewhere on this data continuum. The IT organization can drive significant hidden value across the business by taking the next step toward greater data sophistication.

Let’s take a look at each of these in more detail:

Gen 1 Analytics

  • Data Driven decisions, reduce reliance on “Gut Feel” operations

  • A focus on Statistical Analysis

  • Data warehouse with highly curated data

  • Mostly internal data, limited commercial data like USPS Moves

  • Produce reports and dashboards

  • Data Scientist knowledgeable in both Business and Data

  • Largely reflective

Gen 2 Analytics:

  • A move to “BIG” data—more sources and more data

  • Data Exhaust, Commercial Data Sets, Public Data Sets

  • Transition to a Data Lake without a curated schema

  • Addition of Machine Learning to spot trends in data

  • Make business experts data scientists, not the other way around

  • Still largely reflective but “trends” can be exposed

Gen 3 Automated Analytics

  • Harvesting information exhaust

  • The rise of the API and hidden data

  • Machine Learning applied to Data Scientist role

  • Discovered relationships, grouping, and trends in the data

  • Insights become predictive in nature

  • Insights directly feed back into the business

  • Governance becomes critical

As your data analytics capabilities become more sophisticated, the opportunities available to you multiply almost infinitely. Writing for Harvard Business Review, Thomas H. Davenport and D.J. Patil noted that:

“Companies are now wrestling with information that comes in varieties and volumes never encountered before. If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a ‘mashup’ of several analytical efforts, you’ve got a big data opportunity.”

But how do you use that data in a way that positively impacts your business?

Making the Most of Your Data

There are a thousand creative ways to extract value from data. Here are just a few examples of companies that have automated action to drive value based on data:

Instacart — Instacart uses Deep Learning to optimize shopping and checkout with their retail partners. By analyzing data from shopper behavior over millions of customer orders, the company is able to sort items according to the fastest picking sequence.

Result: Over 618 years of shopping time saved per year.

BMW — Recalls can do serious damage to an automaker’s brand, not to mention the financial ramifications. BMW found a way to proactively address this problem by creating an early-warning system designed to anticipate issues. 

Result: Over tens of millions in recall fines avoided.

Net-A-Porter — Net-A-Porter uses predictive analytics and hyper-personalization to develop better experiences for their customers and predict needs before they arise.

Result: Data integration at all levels of the business to bolster performance.

Keys to Success: Things to Consider On Your Journey Along the Continuum

No matter where you are on the Data Analytics Continuum, success will depend on maintaining a forward view and preparing to take the next step. Here are four things to consider as your data analytics capabilities mature:

  1. Culture Is Still King – Data must become integral to your culture if it is going to significantly impact your outcomes. Decide at the executive level how you will foster a data-driven culture and you will be in a good position to take advantage of new opportunities for growth.

  2. Capture Data Exhaust – Data exhaust refers to trails of data generated through online activity and transactions. This may be in the form of cookies, files, online behavior, and other kinds of raw data that aren’t essential to your business. Even though it’s not part of your core activity, data exhaust can still be a treasure trove of information to help you know your customer, analyze processes, and create better experiences.

  3. Choose the Right Tool – Effectively using your data will mean implementing tools that can capture and analyze it for you. Third-party tools like Qlik, IBM Cognos Analytics, and Domo can help you leverage your data, and you may also want to consider building a proprietary tool to visualize data, integrate data streams into your existing processes, and create more effective workflows.

  4. Consider Privacy – Data holds enormous potential for good, but it can also be used in seriously problematic ways. China, for example, has developed a facial recognition surveillance technology that can be used to screen people for healthcare. The technology scans facial expressions and makes determinations about who is likely to be telling the truth and who is not, with ramifications for insurance eligibility. Deepfakes like those we mentioned earlier are another example of unethical data use. As technology evolves, protection of privacy will rest on the shoulders of companies, and that’s an enormous responsibility.

In the next decade, the public will begin to care about their data like they have about their food in this decade. There is tremendous opportunity to capture and use data to enhance business outcomes and customer experiences. You just have to start.

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