How to Improve BI and Analytics Maturity in your Business

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In the modern, fast-paced and digitalized business world, it is crucial to use data science to your company’s advantage. New and improving analytics technologies make using and understanding complex data easier – but a lot of companies are still behind the curve.

In fact, one study from Gartner estimates about 87% of organizations have low Business Intelligence and analytics maturity. That means most companies are not taking advantage of the data technologies out there and leaving themselves at risk of falling behind their competition due to a lack of access to critical, crucial insights like market trend analysis and forecasts.

Fortunately, that does not have to be the case for your company. This post will cover a few simple, manageable steps toward improving your company’s BI and analytics maturity, to improve your data assets and gain a competitive edge.

What Is BI Maturity?

Business Intelligence has been defined in several different ways. In essence, it refers to a business’s awareness of their market environment, and their capacity to plan, engage and act accordingly using all resources available.

BI maturity can be understood as to how current or modern a business’s methods of BI is. The same can be said for analytics maturity. The tools your company uses to access and analyze data are a reflection of the maturity in both areas – capitalizing on data to improve your business’s decision making and keep up with a competitive market starts with understanding BI and analytics maturity and why it is necessary. It is important to know where your business stands on the spectrum of maturity, to be able to implement change and improve.

Businesses with a low BI maturity are likely to have a much older, outdated IT structure than those with higher maturity. There is also likely going to be limited communication and collaboration between the IT sector and other sectors of the business, and individual units will conduct fragmented analytics projects.

Additionally, low analytics maturity generally indicates data plays less of a factor in decision-making. To put it another way, it is less clear amongst businesses with low maturity that proper data analysis is actually helping to improve business function and decision making. That means the resources spent on analytics are largely wasted. This can be a dangerous cycle for a business that does not fully understand the concept of BI and analytics maturity – the less a business is seeing improvement as a result of analytics, the less likely they are going to be to invest in new technologies and improved analytics.

Again, it is common to have a low measure of BI and analytics maturity – this is an issue a lot of businesses are facing right now, especially as analytics technology continues to advance. However, businesses can easily improve in both areas, mainly by following the lead of businesses with higher maturity.

Improving your BI and Analytics Maturity

Step 1: Collaboration and Strategy-Building

Businesses with a low BI and analytics maturity tend to have a fragmented analytics structure, with different sectors conducting their own analytics projects and poor coordination between IT and the rest of the business.

One result of this fragmentation is what is commonly referred to as data silos, or fragmented, stored data in different areas not contributing to the overall efficiency of the business. You can imagine this concept exactly like a grain silo on a farm; data is stored in one, isolated place, and only used for one or two specific purposes, rather than being spread across the business where everyone can have access to it.

Another dangerous result of the fragmentation of analytics products is the cost in wasted time, money and resources. With multiple analytics projects operating within the same business, and poor communication between them, it is possible for two different teams to be using different strategies on the same data. That means someone’s effort and the cost of analytics tools is being wasted.

There is also a high possibility of error when there is poor communication between analytics projects. Teams of analysts may have different standards according to their department, and therefore vet data differently – this can result in significant errors which could wind up severely costing the company in the long run.

The solution to all these problems? Develop a holistic, company-wide system of analytics and build a unified strategy for problem-solving. The less fragmented a business’s analytics projects are, the more efficient and effective analytics will be.

Better communication between business units is crucial for raising your company’s BI and analytics maturity. That entails adopting the same analytics strategies across departments, using the same platforms to exchange data more easily, and working toward a common goal, while still allowing individual projects to focus on market-specific data.

Implementing this change is not as difficult as it sounds. Consulting with analytics professionals is a good first step into adopting a company-wide strategy and improving holistic analytics within your business.

Step 2: Make Use of Modern Technology and Resources

As we mentioned at the beginning of this post, one of the most troubling features of a business with low BI and analytics maturity is that they are not taking advantage of modern advancements in the field of analytics.

The reason for this, generally, is business leaders do not believe they have the resources to do so, or they do not see it as a worthwhile investment. That reservation is understandable – data science is an emerging and relatively poorly-understood field. Up until the past few years, investing in advanced data analytics technologies has not been common business practice.

However, it is important to be aware a lack of modern analytics capabilities can actually be damaging to a business in the long run, especially as more and more of their competitors begin to adopt them. You should also remember modern analytics software and platforms are becoming increasingly automated and easier to use, even for someone without much tech or analytics know-how.

Experts recommend building a virtual BI team within your business – this will help connect unit leaders in a more holistic strategy, and allow shared access to technologies and platforms. Skills and strategies can be shared throughout the team, helping everybody to learn together and ensuring all business units are on the same page with their analytics projects.

In addition, implementing continuous training for data and analytics teams across the business will help everyone stay up to date. Because modern software is so automated and intuitive today, high skills are not as necessary as they used to be. With minimal training, anyone can understand modern analytics software. It is a small investment in a huge, helpful change for your business.

Step 3: Have a System of Monitoring or Governance

This goes in part with improving the collaboration of analytics projects across business units and developing a holistic strategy.

It is important to have a system of governance for data analysis across your business. For analytics to be as effective and unified as possible, there should be a set of rules and regulations for analysis projects. Data analytics leaders can monitor and implement rules, to ensure all analytics are working in the businesses best interest and prevent wasting resources and funds on unnecessary projects.

There is software available to help you implement a system of data governance. This is a good way of lowering the risk of error caused by different systems of data vetting, as mentioned earlier. It is also a good way for analytics leaders to keep track of analytics business-wide, helping expedite a positive impact on decision making.

Step 4: Build Integrated, Versatile Platforms for Company-Wide Use

Another aspect of capitalizing on modern analytics technology is to create analytics platforms the whole business can use.

Data analytics leaders in your business should be able to build integrated platforms that function for a variety of uses. If everything is operating on one or two main platforms, there is less of a chance of data silos building up or of errors occurring from miscommunication. It can also help to make sure analytics teams for all business units are well trained – if everyone is using the same platform; teaching the skills to use it is easier.

For a platform to be integrated means it involves multiple aspects of BI at once. An integrated platform should include a large data repository – whether a data lake or a more traditional data warehouse, where company-wide data can be stored and accessed. That way, tools and strategies for analytics can be collaboratively built onto that platform. This big data strategy may seem like a tough job at the outset, but it will help to fast track BI throughout the business, improve holistic communication and allow for the sharing and transfer of skills and strategies.

Why Improve Your Business’s BI and Analytics Maturity?

As mentioned above, a lot of business leaders have reservations against investing in improved analytics technology, training, and resources because it does not seem like it is worth the cost. Businesses may be frustrated with the lack of connection between data analytics and improved decision making, or simply be more focused on other sectors.

The reality, though, businesses that do not work to improve their BI and analytics maturity are at risk of falling behind. As advanced analytics technologies become more and more accessible, other businesses within the same field will be making use of them and gaining a competitive edge with the insights derived from data.

Here are a few ways improving your business’s BI maturity by implementing the steps above will help your business stay competitive:

1. Understanding Customer Experience

With a higher BI and analytics maturity, your business will be better equipped to utilize big data to track and understand the customer experience in your field. Have a better idea of what customers are looking for, what sort of tactics have been working with them and what has not, as well as changing trends in customer behavior and inclinations.

Of course, every business is already working hard to track customer experience. A higher BI maturity will help you to make use of the data faster and more reliably.

2. Utilize Predictive Marketing

Using big data appropriately can help a business to stay ahead on trends in their field.

BI and analytics maturity can directly impact effective predictive marketing strategies. Having an older IT infrastructure and poor communication between business units means trend analysis is not as effective as it could be – businesses with better BI maturity will make better use of available data and have more effective predictive marketing.

3. Improved Decision Making

Perhaps the most important consequence of improving your business’s BI and analytics maturity is its effect on decision making. Better, more efficient big data analysis will lead to more detailed insights, improved forecasts and a better understanding of competitors, which will, in turn, result in informed decisions limit risk to your company.

Better decision making is the ultimate goal of data analytics. The higher your business’s BI and analytical maturity, the more accessible that goal is.

Final Thoughts

The vast majority of businesses have a low or improvable BI and analytics maturity. That means the vast majority of businesses are not making use of the data can improve their performance and give them an edge above the competition.

The steps listed in this post were recommended by experts, based on a Gartner report on raising BI and analytics maturity and improving the use of data in any business. They can be implemented without much risk or cost to your business and can have a significant impact on data analysis and decision making in the long run.

A high BI maturity is a necessary step in keeping up with the modern world – every business should take the chance to improve theirs now before they fall behind.

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