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New Thinking about Data Analytics and Data Access for a Stronger Digital Future

Agile companies adopt a modern data analytics approach to prioritize data, enable data access, free up technology teams, and make better predictions that build trust. This new thinking about data is necessary for your business to thrive no matter what.

Part 3 in a series.

In 2020, companies must respond to external influences more rapidly than ever before. Consider the disruptions that have occurred in just the past six months or so alone:

  • Your employees now work at home, and many of them won’t be going back to the office. Your new remote workers need seamless access to more of your company’s data as much as they need their remote work platforms.
  • Your customers expect seamless online experiences. Lost shopping carts, stumbling around your website, and problems finding products or resolving issues are no longer acceptable—even for brick-and-mortar businesses or industries like insurance or financial services.
  • Your business generates unbelievable quantities of data, and your customers and employees are consuming it rapidly. In May 2020, IDC’s Global DataSphere senior vice president Dave Reinsel said, “Instead of hindering growth, the COVID-19 pandemic accelerated it, creating a surge in video-based content consumption and disrupting the Global DataSphere growth trend, especially in 2020 and 2021.”

To survive in our new data-rich—and data-dependent—environment, companies need models that can intelligently manage their information so they can access it easily both internally and externally, free their technology staff to do their jobs, and make reliable predictions that build trust among customers and employees.

Companies that can embrace the people, process and technology transformations needed to meet rapidly evolving expectations and behaviors will have the advantage as they navigate what’s next.

Make Sense of Your Data with Prioritization

To adopt a modern data analytics approach, agile companies must first recognize that while all data has value, not all data has equal value. To succeed, you must prioritize the most important data over the less critical data.

Because you may not yet see the value of some data, you should start by acquiring and preserving your raw or “dirty” data in a repository that your entire enterprise can access.

One tool our clients use to accomplish this is the data lake. Think of a data lake like a city’s water reservoir. In the same way that a reservoir stores water for future use, a data lake stores information for later analysis. Tools such as Azure Data Lake replicate data in near real-time from its sources, allowing users to access the replicated data in the lake as they analyze it without affecting the source data.

As you can see in our e-book, “Defeat Property & Casualty Insurance Challenges with a Modern Analytics Approach,” data lakes are well suited to industries such as insurance and financial services. Still, you can apply it to any organization managing large quantities of data. It is a true data transformation approach. Instead of simply installing new data analytics software, you completely overhaul how you think about data.

Another tool gaining traction is Snowflake. A database platform like SQL Server or Oracle, its developers built Snowflake for the cloud. They integrated familiar components such as tables, views and SQL queries with new thinking about how we should use databases. The result is a platform that builds on the cloud’s power to separate storage and computing while implementing massively parallel processing (MPP), pay-as-you-go usage and on-demand scaling, and virtually infinite storage.

It takes work, but with these tools and the expanded use of languages like R and Python, you can identify, ingest, aggregate and use your data without fear of disrupting your business—which allows you to serve your customers and employees better. These are just some of the ways the most modern, agile companies are moving from traditional data warehousing to faster, more nimble ways to access and use large amounts of data.

Benefits of Modern Data Analytics

Enable Data Access

In digital-first organizations, the company stores all applications and processes in the cloud for easy access. Once data is in the cloud, you can work on building applications and user interfaces that allow users to find it quickly.

Free Up Your Technology and Data Teams

Many organizations find their technology staff overwhelmed with data requests that take them away from their critical work of keeping your systems running. In an intelligent enterprise, leaders clearly tie data to business needs and make it accessible to everyone who needs it, when they need it. Data is in the cloud and delivered through user-friendly interfaces.

Make Better Predictions

Modern, digital-first companies also use machine learning (ML) and artificial intelligence (AI) to help them automate data management and make better predictions, faster. ML and AI analyze huge amounts of data quickly to identify patterns that anticipate future trends. These statistical methods further help you identify the most relevant data and separate it from the noise. As your predictions are proven out, you will build trust with your customers and your employees.


We know that not every business was at the same point on their digital journey when the COVID-19 journey began. By understanding your technical capabilities, data maturity and culture, you can find ways to become a stronger, more data-savvy organization more quickly and more easily. So, while we may not have all started at the same place, we can now all work together for a stronger digital future.

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