Data is key to successfully solving business challenges. The problem is that business leaders don’t always know what data to use, where to find it, and whether it’s trustworthy. Just importing and analyzing data is not enough. Most organizations and individuals don’t even use data – they rely on intuition to statistically be often more wrong than they realize.
The business intelligence (BI) maturity model enables business leaders to improve their businesses by understanding the role data plays in their organizations. Increasingly sophisticated business intelligence provides companies with better quality information to make better decisions. Every step closer to business intelligence maturity is a step closer to proper data governance, the use of collected data to make informed decisions.
What is the Business Intelligence (BI) Maturity Model?
The Business Intelligence (BI) Maturity Model is a five-level scale that tells organizations how mature their data and analytics strategies are. The BI maturity model’s levels are measured in terms of the value provided to organizations and the maturity of their tool suites.
The lowest level of business intelligence maturity is characterized by a limited number of data sources, rules, and decentralized metrics reporting within an organization, resulting in a disjointed and somewhat inaccurate picture of the company.
The highest level of business intelligence maturity is characterized by strategic, tactical, and operational decisions involving numerous factors and variables. Organizations using advanced tools can model their business behavior and accurately predict future outcomes effectively.
What are the five levels of the Business Intelligence (BI) Maturity Model?
1. Unaware
Organizations at the low end of the BI maturity model have data scattered across disparate, unrelated spreadsheets and documents. Employees may want information, but they only ask for it infrequently. With no one in charge of data management, companies at this level rely heavily on their intuition and apply ad hoc analysis. There are no formal processes, procedures, or practices to support BI.
2. Opportunistic
Some business intelligence solutions are available to a limited number of users, distributed throughout the company, and utilized as independent projects. Individual departments prepare data according to their needs, but databases facilitate the possibility of using BI tools.
3. Standard
At this level, technical standards emerge, and some processes are assumed to be implemented across the organization. Data generation is consistent – reports, dashboards, analyses, and conclusions are shared across different departments. At this level, companies create cross-functional teams responsible for organizing and effectively using business intelligence solutions.
4. Enterprise
The implemented BI solutions are mature and widely used to support decision-making. All employees, from analytics to senior management, consistently rely on analytics and business intelligence tools in their daily work. BI cross-functional teams organize into departments with a broader reach.
5. Transformative
Companies at the higher end of the BI maturity model have a Chief Data Officer (CDO), or at least someone responsible for data curation. Employees trust the data they receive and use it to its fullest potential—increasing sales, improving efficiency, and reducing costs. Employees increasingly rely on data for most decisions. Transformational technologies such as artificial intelligence are used to achieve business goals and aid decision-making.
How to increase the BI maturity level to empower your business?
The first step in deciding to rely more on data for decision-making is to identify where you are on your business intelligence maturity journey and where you want to be. The resulting roadmap sheds light on where you are doing well, areas for improvement, where you need to go next, and how to get there.
Start with a strategy
The business intelligence maturity model provides an initial framework for organizations, but their long-term strategies should not rely solely on a single model. Once implemented, data and analytics strategies should focus more on what competitors are doing and how to match and surpass them. Start by developing a one-year data and analytics strategy. It would be best to have clear milestones in mind during the first year and when you expect them to be completed.
Focus on quick wins
Your plan should focus on delivering quick wins that show the entire organization the value business intelligence software can bring from your data. With a fast, agile approach to BI, data, and analytics, you can gradually scale up for more significant business impact. However, the quick wins should help you achieve the long-term goals of a data-driven strategy.
Connect software to strategy
You may need business intelligence software, but know why you need it. If you buy software that doesn’t have a clear purpose, you’ll end up wasting thousands of dollars. The BI software purchasing choice should stem from strategy needs, not the other way round. Your software should help you achieve solid, reasonable business goals.
Build a makeshift BI team
Instead of spending time and money building a dedicated business intelligence department, create a makeshift BI team consisting of stakeholders from existing departments, including business and IT. Task the team with setting up your BI strategy and launching it.
You should ensure that your data and analytics programs meet the company’s needs so that employees are ready and able to act in a data-driven manner. Your new team shouldn’t be the powerhouse behind the shift to data maturity. Instead, their goal should be to develop a strategy that encourages grassroots interest and participation in analysis.
Set up a data governance framework
Building a governance framework starts with identifying what data you have. Find out what information you collect and where it resides. Setting your governance strategy means developing a plan to keep your data clean, accurate, usable, and secure. If you don’t have a governance framework upfront, it will be hard to do later. It could also mean setting up your business intelligence software so users can access what they need without access to everything.
Encourage collaboration in data governance
Data governance should not be viewed as a constraint but as a protocol. When your governance strategy is developed collaboratively, employees see governance as a joint effort rather than coercion. The governance framework should be built through collective work, with business units sharing their best practices and working with IT to make governance a shared enterprise.
Conclusion
The availability of up-to-date data-driven information leads to better business decisions and improved business performance. The ultimate goal of a BI solution is to provide decision-makers with trusted data that supports their BI strategy — accurate, information-based decisions. To achieve this, companies must develop and align business intelligence strategies and roadmaps for their implementation.
Read more on this topic in our previous article: “Where are You on The Business Intelligence Maturity Model?”
At Itirra, we provide companies with customized solutions and opportunities to explore potential improvements to support their business goals. For a personalized recommendation based on your unique business model, don’t hesitate to get in touch or schedule a meeting with me.