When a company invests in business intelligence (BI) technologies, it’s generally after a serious period of deliberation. Why? Because it represents a significant investment — and enterprises can only reap all the benefits of BI with the right tools at their disposal, helping them maximize their return on said investment and improve performance.
Here are some key points to keep in mind for companies aiming to get the best out of the BI technologies they end up implementing.
Choosing the Right BI Tools
Unlike the chicken-and-the-egg conundrum, we know business goals and strategy must come before BI tools — never the other way around. Only by fully understanding your company’s needs in terms of BI can you choose a platform capable of addressing them effectively.
Some questions that can help your organization narrow down what it needs from its BI tech include:
- What internal and external sources of data are we aiming to integrate?
- Do we want a cloud-based, on-premises or hybrid solution? The answer will depend on your IT infrastructure and
- How many users will start out using these tools? How much user base growth is anticipated?
- What self-service business intelligence capabilities can our company benefit from? An example here would be search analytics, which allows individuals to ask ad hoc questions whenever they need rather than relying on canned reports.
- What are the costs of BI tools, both up-front and over time? For instance, if your organization is growing rapidly, choosing a scalable solution without per-user licensing fees may be advantageous.
Taking the time to match BI technologies to your exact business objectives — as well as current and future needs — will help ensure the tools remain highly usable and relevant out of the box as well as over time.
Eliminating Barriers to BI Adoption
Another angle to getting the most from whichever BI tech your enterprise selects is systematically eliminating barriers standing in the way of user adoption. After all, deploying the tools is just the first step; how much value your company is able to derive from them in terms of decision-making depends on users actually harnessing them.
As research firm Gartner has outlined, driving adoption of BI and analytics tools across the workforce has presented a major challenge for companies. In 2017, they figured adoption reached only about 30 percent of all users.
This is why identifying and eliminating barriers to adoption is an important part of helping the workforce embrace these tools.
Raise Data Fluency Across the Org
People tend to engage with data if they feel comfortable doing so. While choosing self-service BI software is an excellent start, some data-shy employees may benefit from a tutorial on how to use the interface. Training on how to drill down into charts, accurately interpret insights, create dashboards and embed insights into common workflows can also help empower employees to feel “qualified” to harness data tools to the fullest. The more specific and actionable the data fluency training based on role, the more effective it tends to be.
Embed BI Across the Ecosystem
Instead of conceptualizing BI as its own standalone platform, consider all the ways in which BI tools and insights can embed into existing applications. This helps put data directly where people spend time, minimizing the time it takes to visit a separate window — and reducing the chance of distractions and miscommunications.
Build Data into Company Culture
Far from existing in a vacuum, BI tools either complement or clash with how company culture regards data. Building a data-driven culture is a must. This means leaders must not only talk about data freely, but also utilize it in their own roles. As well as the analytics tools, the language and expectations surrounding data should be democratized across the company. A company with cutting-edge tools but a culture unsupportive of data in decision-making will miss out on the best of what BI can offer.
Getting the best out of BI systems means choosing tools that align with goals and needs and tearing down barriers standing between users and positive data outcomes.
