5 Building Blocks of a Comprehensive Business Intelligence Strategy

Making the best use of data is the key to success for any enterprise today. With so much information available to assist in building a strategy, there's no excuse for relying on hunches or previous actions to determine your future direction.

But turning this wealth of resources into usable insight is a challenging process, both in terms of the technology needed to analyze data and the human factors involved in managing and interpreting it. That's why you need a comprehensive business intelligence (BI) strategy in place to address these issues.

Business intelligence vs big data: What's the difference?

Business intelligence may be viewed as being somewhat interchangeable with big data analytics, but there are a few nuances to be aware of. Whereas the main purpose of big data is to capture, process and analyze very large quantities of data, BI is more concerned with extracting insight from this data and turning it into accurate, easily-understood reports that can be used to guide decision-making.

The two also use different toolsets. Big data tools are primarily designed to store and process large amounts of data, whereas BI software aims to collate and visualize it in useful formats. Business intelligence solutions also rely only on more structured, historical data to identify patterns and trends, whereas big data can take in a wider variety of unstructured sources. However, to be successful, both BI and big data will have to work in tandem.

The importance of an effective BI strategy

Having an effective strategy is vital for making the most of your data assets. Without a single, comprehensive solution for managing your data, you'll find many different approaches emerge, which can prevent you from gaining a single version of the truth - something that’s vital if you want to make the most accurate decisions.

Firms that lack a BI strategy may find different teams conduct data analysis in their own way. They could use their own datasets or tools to ask questions of their data. Even if they do have access to the same resources and analytics technology, the conclusions they draw could be different if the outcomes aren't presented in a clear, consistent way throughout the business.

5 steps to develop a comprehensive BI solution

To prevent these issues, you need both the right business intelligence software tools and a clear plan for how these solutions will be integrated into the business. With this in mind, here are a few essential steps you'll need to bear in mind.

1. Understand your business goals

No BI tool can be successful unless everyone using it is clear on the vision behind it and what the firm is trying to achieve. This means talking directly to business units to understand what kind of insights they need and the questions they're trying to answer. Once the IT department has this information, only then will they be able to to craft a solution that meets these requirements.

2. Create a clear BI roadmap

A comprehensive business intelligence roadmap should spell out exactly what the plan is for implementing your BI solution, and do so in an easy-to-understand format that can be referred to at a glance. This should include all deliverables or milestones, as well as make clear exactly which teams or individuals will be responsible for which activities. You can go into as much detail as you like, but to ensure convenience, it may be a good idea to stick to high-level goals and targets (such as 'find a BI vendor') so everyone can see what the priorities are.

3. Develop strong governance processes

In terms of BI, governance refers to how the infrastructure for the system is defined and managed. This is different from data governance activities, which are a critical part of any big data analytics strategy. For BI, you need to set out a governance team who can take responsibility for the tools and processes. This includes lifecycle management to ensure the continued development of tools, as well as user support activities to answer any queries and solve issues that may arise.

4. Ensure your team embraces the culture

Addressing your company culture to ensure everyone is on board with the strategy and recognizes its value is a must. For instance, this ensures all teams are using the same single source of truth for their decision-making and have faith in the accuracy of their data. This requires a good change management plan and must start at the top. If senior personnel are able to demonstrate how they use business analytics strategies in their own decision-making, this will filter down to other departments.

5. Democratize your data

A key part of building user trust in the strategy is to ensure that all employees can access data analytics tools and create their own queries. If analytics and insights activities themselves are left solely to data science teams, this can lead to processes taking longer and answers being less relevant. To improve this, you need solutions that make it easy to ask questions of data and visualize the findings to put insight into the hands of non-technical business users.