Data Powers Digital Transformation
For organizations that are beginning to use data to deliver new insights, analytics isn’t just a tool — it’s a journey.
Leaders within most of the organizations I work with realize that data analytics must be a central component of their digital transformation efforts. But they often don’t understand the work that a robust analytics program entails. More than once, I’ve spoken with executives whose firms have purchased advanced analytics tools, and they’re confused about why these solutions aren’t producing the results they expected.
The explanation usually lies with the work the organization has (or hasn’t) done with its data before adopting an analytics solution. Analytics tools are powerful, but they aren’t magic. Only companies that put in the necessary work will see the biggest benefits. In our experience at CDW, organizations typically follow a five-step journey toward analytics success.
1. Manual Reporting
At this early stage, users are manually pulling data from different systems, manipulating the information themselves using simple tools such as spreadsheets, and sharing it with one another via email or cloud collaboration tools. While manual reporting can help deliver some basic insights, it’s a time-consuming process that is prone to error and doesn’t allow for truly advanced analysis. Nearly all organizations at this stage will greatly benefit from the enhanced analysis and collaboration features of a dedicated analytics tool.
2. Basic Visualization and Business Intelligence
An organization that is engaged in basic data visualization and business intelligence has typically purchased a license or two for analytics software but hasn’t yet developed a cohesive strategy for data analytics. Such firms are vulnerable when their data-focused employees leave the organization, as there often isn’t a plan in place to carry on that work. Additionally, companies at this stage usually haven’t created a comprehensive inventory of their data, meaning that even the few employees working on analytics might not have access to important information.
3. Data Methodology and Strategy
Here, organizations create a unified strategy to improve decisions and actions using data, maintain data integrity through governance and turn managed data into an organizationwide asset. This is where companies turn the corner on their journey to robust analytics.
4. Advanced Insights and Modeling
Once an organization has standardized its data and created a cohesive strategy around analytics, it can begin using tools to arrive at deeper and more meaningful insights. At this stage, a company will uncover more use cases for analytics, move beyond visual data representations toward deeper analysis, and build models that supplement human interpretations of analyzed data.
5. Predictive and Prescriptive Analytics
This should be the end goal of any organization that is serious about making the best use of its data. At this final stage, analytics engines don’t merely help companies make sense of their current data around sales, customer, inventory and other systems, they also help to predict future behavior. Over time, organizations at this level will work to increase the accuracy and efficiency of operational recommendations. They will also increase the number of data sources they use — potentially incorporating public data — to test their strategies and continue to improve decisions.