Recently we were watching a Gartner Webinar: Effective Business Analytics Strategies for Midsize Enterprises. (If you want, you can watch it on demand here.) We found the webinar really interesting in terms of what is differentiating midsize enterprise organizations in terms of those that are having more success with their business intelligence initiatives. Here is a quick summary of what we took away:
- First for reference - Gartner considers companies to be mid-size enterprises (MSE) if they are under $1B/annual revenue and/or under 1,000 employees.
- Across the board, the most successful MSEs are putting more emphasis on data.
- To get the most success MSEs need to reverse their approach to business intelligence/analytics and put business outcomes ahead of technology.
That's right, when many organizations look to start or expand how they use data to grow their business, they look into what technologies they need to invest in before they decide what they want to achieve with their data.
While that may work out okay, leading with technology may mean your technology solution falls short or makes it more complex to find the answers and insights you need.
Alan D. Duncan the Research Vice President that hosts the webinar recommends, that companies should determine their desired business outcomes before making any technology decisions. In other words, be able to answer: "Why are we doing this?", "Who are we doing this for?", "How are we doing this?" and "What are we going to deliver?" before moving forward with a technology solution.
As you look at the Gartner diagram, if you are feeling stuck, don't worry. Our team of data experts can help you with the planning or the deployment, selecting the right technology, making sure you have the right business context and of course the right team to put it all together.
If you have any questions give us a call, or if you are already in the process of selecting your business intelligence solutions, be sure to check our latest guide where we do a side by side comparison of Tableau, Spotfire and Power BI.