Data is a critical business asset. Few will argue that point. Better data leads to better decision-making. Again, no argument. So how do you make the business case for data analytics?
Where the debate begins is whether embarking on an enterprise-wide data analytics project, or investing in a new business intelligence (BI) solution is the right thing to do from the company’s perspective. In the minds of many business leaders, jumpstarting a new initiative and deploying new software, equipment, storage space and training can be expensive, time-consuming and fraught with potential problems. They need to be convinced that BI will ultimately deliver bottom-line benefits.
Among those with little experience in BI or analytics, there is a fear of the unknown, an uncertainty of the true value of BI and a doubt of promises left unfulfilled. Knowing the value of data and how important it can be to the success of the business, data analytic and IT leaders must establish clear connections between analytics and business. They must also work to overcome stakeholder objections. By doing so, they can help eliminate the fear, uncertainty and doubt coming from business leaders and prospective users.
What can analytics, BI and IT leaders do to make the case for business intelligence? There are a number of things that the IT team can do, we have addressed three:
1. Focus on the drivers that matter to the business leaders and stakeholders.
Too often the teams and departments interested in an analytics solution limit their focus on technology and delivery rather than business benefits. What the pandemic has helped demonstrate is a greater understanding of technology and its alignment with business benefits. However it's still important to describe a strategy that connects both emotionally and rationally to the stakeholder's aspirations, ideally developed collaboratively by business and technology stakeholders.
This means that BI and analytics leaders must present positive outcomes that benefit both individual stakeholders and the organization as a whole.
2. Demonstrate how analytics will help company executives, as well as other stakeholders.
It’s important to show that analytics can influence decision-making, providing a clear and comprehensive picture of business performance and driving business objectives. Everyone, from business leaders to salespeople, can have access to the information and reporting they need to be more successful.
3. Link analytics to business outcomes.
Pretty charts and graphs will not sway opinion unless they can show how business outcomes are influenced by the analytics and the underlying data. The ability to show how business intelligence influenced revenue-generating opportunities, increased operational efficiencies and improved customer services will strengthen the business case for analytics.
Drivers that can be used to justify an investment in BI include:
- improving business processes
- improvements in centralized data management to ensure a consistency and control in maintenance of information
- better data quality
- reporting can be managed in the BI solution; removing the burden from other business systems.
Ultimately there are numerous business benefits that come from implementing an enterprise BI initiative. The majority of corporate data around the world is unstructured making extracting value from the information a difficult process. BI converts the raw information into actionable insights, enhancing internal analytical capabilities and eliminating the “gut-feel” approach to decision making. BI also makes data universally accessible to everyone across the business, which allows for more timely and informed business decisions.
In Gartner's FrontRunners Business Intelligence Report this year, Phocas is the clear leader outperforming all 24 other data analytics products reviewed.
Phocas scored highest in both user categories with a score of 4.5/5 for “usability,” which is a weighted average of “functionality” and “ease-of-use." Phocas also achieved the top score of 4.75/5 for "customer satisfaction", which is a weighted average of three user ratings, which are “value for money,” “likelihood to recommend” and “customer support.”
To find out more, download the eGuide: Building the case for data analytics.