In a previous post, we explained that to succeed in today’s fast-paced, hyper-competitive market companies must be data-driven. But it’s not enough for companies to simply gather data.
Top-performing companies are able to differentiate and distinguish themselves in the market through their abilities to use the right data at the right time to make immediate decisions and act quickly to achieve operational and/or business success.
Of course, becoming a data-driven organization doesn’t occur in a vacuum. Data and analytics are deeply ingrained in the cultures of companies that consistently outperform their rivals.
The true measure of success in today’s data-driven economy is centered upon an organization’s analytics maturity. At TIBCO Spotfire, we view analytics maturity as a six-stage journey.
In the first stage of analytics maturity, organizations use analytics to measure different aspects of business performance, e.g., actual sales to target.
The second stage of analytics maturity is diagnose. As organizational leaders are alerted to a change in their business (e.g., a sudden drop in regional sales or a spike in Net Promoter Score), they can use analytics to drill down into the data to determine what caused this to occur. Armed with this insight, they can capitalize on an opportunity or correct course if needed.
As companies grow their analytics maturity, they use analytics in deeper fashion, to predict and optimize. This is the stage where opportunities for strong competitive differentiation begin to surface, giving organizations the ability to anticipate trends and optimize operations.
The next step is to operationalize the use of analytics so that it’s embedded into day-to-day business processes in a manner that’s easy for all business users, enabling them to take data-driven actions.
The automate step in the analytics maturity model allows the business to act in real-time, as critical events surface. Leveraging analytics and automation to let humans – or systems – act immediately to take advantage of opportunities or to detect and mitigate risk.
In the final stage, transform, organizations have successfully evolved into the data-driven culture, where analytics is naturally embedded in the business mindset. Analytics enable all users to cogently address all challenges, opportunities, and risks.
In our next installment in this series, we’ll examine the elements that make up the first stage of the analytics maturity curve (“measure”), including the benefits that can be derived through these capabilities, the key barriers that must be overcome to achieve success, and how this helps to lay the groundwork for more advanced levels of analytics maturity.