If you haven't updated data analytics strategy in the past 18-24 months, it’s probably obsolete. How can you keep it fresh?
Successful business analytics begins with an understanding of desired outcomes, a strong use case, accountability, and alignment.
Most organizations would claim they have a massive amount of data. But how much of it is useful? Are your employees gaining real insights from your data analytics and the massive volume of data—internal, external, structured or unstructured—that’s flooding your company? Or, are you experiencing the reverse: is incomplete (poor-quality) data creating additional risks to your business?
In either scenario, the issue remains the same: You need someone in place to manage these efforts, prioritize initiatives, and create clear business outcomes.
Data science and the language of descriptive, predictive, and prescriptive analytics is the “new normal” that can help your organization create this culture. To exploit new sources of data, new resources and skills are an imperative.
There are three key steps you need to take:
Align: Align use cases that specifically target intended business outcomes, like greater customer satisfaction and customer retention or, of course, increased sales.
Establish: Establish the leaders who are responsible for the formulation, strategy and implementation of those use cases.
Provide: Provide continuous feedback to determine the effectiveness of the use cases—even, if necessary, their obsolescence.
In addition to taking these key steps, using case identification and creation can bring clarity to thinking about the business. Remember, building the right mix of business and IT leadership is an imperative to driving and aligning outcomes. Enabling governance for data and outcomes provides a consistent, continuous stream for both business and IT stakeholders.
These activities provide the necessary information to help you determine whether to continue, expand, scale back or terminate the use case.
This three-step, use-case-driven approach can lead to data analytics initiatives that achieve breakthrough value.
For example, one of our clients manages loyalty programs focusing on restaurant point-of-sale data, including data specific to customers that redeem rewards at the restaurants.
When a person signs up for a loyalty program, a huge amount of data gets generated and captured. The data ranges from personal details, demographics and location to web behavior, email addresses and website usage data.
Our use case aligned business and IT teams to drive new analytic insights to their end-users’ target customers.
It delivered personalized marketing campaigns that gave targeted customers contextualized products and services—resulting in additional revenue and increasing customer satisfaction.
It created a platform for our client’s marketing data scientists to identify new data correlations. Those new correlations enhanced customer loyalty and identified fraudulent activity.
Restaurant managers leveraged dashboards to optimize supply chain KPIs in real time.
The keys to a well-governed analytics strategy begin with a strong business use case, priorities that are realistic, socialized, and agreed upon, and an understanding of the desired outcomes and associated risks.
The journey to create value from this analytics strategy is almost never a straight line. But businesses can stay on the path to creating an analytics-driven strategy that can deliver predictable success.
Instilling discipline in how to organize big data, who owns it, and how it fulfills the needs of the business both operationally and competitively can create the context for effective predictive analytics and directed innovation.
It’s the first step towards creating a data-first organization. Have you been able to take this step?
Does your organization need help creating a data-first strategy?