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TDWI Upside - Where Data Means Business

Overcoming the Roadblocks to Analytics Adoption

To maximize the benefits of analytics, you’ll need the right combination of data strategy, technologies, and well-trained users and analysts.

Data is one of an organization’s most valuable assets, up there with people, leadership, and creativity. When used correctly, data can unlock valuable insights to help anticipate demand, overcome operational challenges, and stay ahead of the competition. Research from Gartner revealed that more than half of organizations (54 percent) want to use data and analytics to improve their process efficiency, with enhancing customer experience and new product development tied in second place (cited by 31 percent of respondents).

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Although analytics adoption is rising, few enterprises are seeing substantial benefits. The Gartner survey also shows that only 9 percent of organizations worldwide feel they’ve reached a transformational level of maturity in BI and analytics, despite this area being a number one investment priority for CIOs in recent years. Only 44 percent of respondents in North America and 30 percent in Europe, the Middle East and Africa think they’re getting any differentiating or transformational benefits from their data analytics programs.

Given that running a data-driven business is widely acclaimed in the business community, it may come as surprise that so few have fully realized a completely data-driven model. It’s not because companies are not using their data. They are simply trying to understand what it is they have. This is not surprising considering the high volume of complex data that technologies are pumping out.

The Data Trap

Eager organizations that don’t first determine how to resolve the challenges of adopting data analytics can fall into a futile trap. First, they have to search for their data. Whether in proprietary applications, departmental business intelligence databases, or accumulating in data lakes, locating data for analysis can feel insurmountable. Adding to this is the proliferation of dark, fragmented data; unknown and unseen, this valuable data gathers dust instead of insights. The cherry on top is that most organizations still rely on legacy infrastructures, which can result in inefficiencies and latency in the data pipeline.

To overcome these challenges and speed up their digital transformation, many organizations may simplify or extract insights from limited or aggregated data, which yields only broad results that provide little value.

Thankfully, overcoming the obstacles to data analytics is far less complex than the technologies that power this valuable business benefit.

A Better Beginning

From the beginning, make sure your organization has a clear and future-proof data strategy. You want to know where you’re heading before you transform your analytics environment. At first, this might seem insurmountable, especially because many organizations have established data silos with different (and barely integrated) applications. However, it’s worth taking the time to build a complete picture of what data the organization has and to make sure everyone involved with data in your organization has a clear idea of what you’re trying to achieve with it.

Many organizations choose to move all their data into the cloud in one step, crossing their fingers that everything won’t implode. This can be risky, not to mention stressful. A better approach is to take measured steps with small-scale projects to test the waters. It’s also important to consider whether your goal is to have everything in the cloud or only a portion of the data there (with the rest remaining on premises for business and regulatory reasons).

Of course, the right data strategy is one thing. Finding the right technologies to deliver it is another. It’s important to have one eye on the future and think about the expandability and sustainability of technologies you choose for your cloud project. For example, there has been buzz around rising technologies such as Hadoop’s scalable HDFS storage, NoSQL/NewSQL systems for data processing, and streaming solutions such as Spark or Kafka in the past few years. A fast analytics database is key for running predictive analytics at scale. Combined with in-memory technology that can be integrated with existing systems, making it suitable for every conceivable cloud scenario -- from public to private to hybrid -- such an analytics environment helps organizations stay ahead of the competition.

What is a suite of technological tools without a user base that champions them? Most important in tackling the challenges of data analytics is data education as well as ongoing training and development. On the one hand, decision makers need to have the necessary data literacy to make informed and actionable decisions based on the data they have. Business users need to understand and evaluate their information and put it into a strategic context. On the other hand, data analysts and data scientists (as well as others heavily involved in working with data) need ongoing training to ensure their analytical skills remain sharp and their technical expertise grows.

A Final Word

When successfully implemented, data analytics can be transformative for businesses, delivering considerable competitive advantage. It enables organizations to improve profit margins, improve customer satisfaction and therefore retention, drive innovation, and help generate new business, when used effectively. A combination of the right data strategy, appropriate technologies, and well-trained users and analysts is critical to maximizing those benefits.


About the Author

Eva Murray is the head of business intelligence at Exasol. In her career, Eva has worked across the spectrum of analytics, from providing consulting services to working in the finance industry as a data analyst to now leading BI at Exasol. Eva is a recognized leader in the data visualization industry and has been named a Tableau Zen Master for the second year in a row. She co-hosts the popular global social data project #MakeoverMonday and has co-authored a book on data visualization best practices. You can reach the author via email, on Twitter, or LinkedIn.

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