Business Intelligence (BI) is the procedural and technical infrastructure that collects, stores, and analyzes the data produced by your organization. It encompasses data mining, process analysis, performance benchmarking, and descriptive analytics.

Business Intelligence (BI) and the Analytics Maturity Model

When it comes to Business Intelligence, there is a general lack of understanding as to where exactly your organization may be situated with respect to the maturity curve and, perhaps more importantly, how to move through it properly.

To identify your organization’s position in the BI maturity model curve is a simple yet incredibly important exercise because we can all agree that most C-suite executives would want their organization to be as data-driven as possible. For their digital transformation initiatives to be truly fruitful, they must harness and execute on the potential of their data.

There are plenty of definitions for analytics and maturity curves as defined by analysts recently, but before we get into that, let’s assess your BI’s state of affairs.

When it comes to your own organization, consider the following:

• Are you missing the big picture?
• Are you seeing multiple versions of “the truth”?
• Do you have report and data silos?
• Do you have to deal with data issues?
• Are you looking for results, insights, and predictive data?
• Do you hear from divisions/departments about a lack of access to data and reports?
• Are you experiencing report security concerns?

If you answered no to all of the questions above, you can quit reading now because you’ve already achieved Nirvana in your BI journey.

If your answer is yes to at least one of these questions, then there’s work to do. Keep reading.

Stages of BI Maturity

Let’s talk about the stages of business intelligence vs. productivity for an organization.

Basic

This is the earliest stage of BI adoption, but is the bucket into which 87% of all the organizations fall, according to a Gartner report. Broadly speaking, this is a very promising stage and is characterized by raw data usage, ad-hoc individual reports, and at best some operational reports for a group of users.

Most of the decision making in organizations is intuitive or based on excel sheets. It wouldn’t be incorrect to call this a “pre-BI” stage.

Let’s drill down a bit into those main characteristics.

Raw data

This is the lowest stage in terms of analytics. The main characteristics are:

Any reporting is difficult and dubious

Because the data estate is completely unorganized, it becomes difficult to stitch together the different bits of data for any analytics. Whatever reporting you do get is difficult to rely on because of the (missing) quality of data.

Getting basic information is a struggle

Individuals who might be looking for data-based insights have a tough time doing so due to a lack of formal processes, procedures, or practices prepared to support BI.

Severely limited analytical capability

The analytics is limited mostly to excel sheet and ad-hoc data extracts required by pockets of people.

Ad-hoc/Operational reports

This is one step above the “pre-BI” stage.

During this phase, some users may implement BI solutions but mainly for their own use. The nature of the reports is reactive and descriptive, serving mostly as a day-to-day operational perspective.

This may primarily involve performance evaluation, cost monitoring, or any current issue that forces users to periodically analyze the data.

Progressive

At this stage, technology standards start to emerge and some process assumptions are typically implemented across the entire organization.

There are BI generated in the form of Data Marts and Data Warehouses – comprising reports, dashboards, analyses, and conclusions that are shared among different departments.

A Business Intelligence Competency Center – a cross-functional group of people responsible for organized and effective use of business intelligence solutions across the entire organization – also starts at this level.

Data Warehouses and Data Marts

As the organization becomes active in realizing the importance of analytics, we see efforts towards building an enterprise data warehouse.

Typically, it starts with some department or division getting their data act together by building a data mart around their line of business.

Gradually we see the trend setting in where other departments also demand their own piece of BI and it finally becomes necessary to get an enterprise data warehouse (EDW) built.

The focus here shifts from localized, operational reports to enterprise, strategic analytics.

An EDW is a tall order and can be a very challenging task for big organizations. There are a variety of stakeholders involved, which can result in all kinds of friction ranging from ownership of data and definition of metrics to the politics of a turf war.

That said, it is still a goal worth pursuing because the outcome of a successful data warehouse far outweighs the cost and effort. It establishes the standard, brings about a data culture, and sets the expectation at the enterprise level. 

Self-service visualization and data democratization

With the advent of analytics awareness and usage across the organization, the business analysts become BI champions and the need to rely on IT team for every little change in a report/dashboard is seen as a bottleneck.

Also, data is started to be seen as an enterprise resource that everyone should have access to. This results in efforts towards providing a self-service-based reports/dashboard data model. This is when tools like Power BI, Tableau come into the picture.

Data sharing and exchange between user groups and departments is encouraged and facilitated. The most common way to facilitate this to is take the data to the cloud and utilize services to enable data sharing.

Azure data platform, Snowflake, and AWS are leading choices for data democratization.

Evolved

This is the advanced level of BI maturity and the organizations in this space can truly call themselves data-driven.

The focus here is building an enterprise-wide data and analysis (D&A) platform which is seen at the core of a digital transformation strategy.

The goal is organizational transformation and establishing a robust data culture – enabling best data practices and utilizing the latest and greatest tools and techniques in descriptive, diagnostic, predictive, and prescriptive analytics.

Data science and visualization

A D&A platform provides rich analytics, including rule-based and predictive analytics, clustering, simulation, and statistical algorithms. Users can explore machine learning and cognitive capabilities to discover new use cases of D&A becoming available through artificial intelligence.

To visualize that data, interactive dashboards and business intelligence reports are designed. The BI applications are designed in a way that your customers, employees, and partners can intuitively understand.

A thorough and complete Data Governance

D&A platforms support a variety of service models on-premise, in the cloud, or hybrid-hosted by a vendor and/or the company. There is a consistency in data governance and data quality that is maintained through enterprise-wide rules and framework.

High-security data centers, data encryption, and identity and access management are leveraged. There is provision of data discovery and catalogue ensuring effective exploration, sharing, security and democratization.

Is your organization’s BI where you want need it to be?

Learn more about measuring your data maturity and how to propel your organization’s Business Intelligence solutions forward.