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Data analysis platforms require constant maintenance and reformation from the second they are born. The system’s development necessities however are being influenced by the world’s ever-advancing technology and the company’s inner focus on business-analytics.
In the previous part of our blogpost we introduced the concept and circumstances of a BI platform’s birth and now we are digging into the relationship between the complexity of a given BI solution and the company’s level of data-maturity.
The exponentially growing data in the world is causing such a competition on the market that forces businesses to make effective decisions based on data. Data-driven decision making however cannot be considered as a static operation, as opportunities and risks will always be arising due the rapidly changing environment and the company’s technology continuously needs to adapt to that.
Traditional simple tables and two-dimensional visuals nowadays are actually considered as disadvantages. Their places have now been overtaken by interactive dashboards, queryable interfaces and complex reports summarizing business insights. Modern BI solutions are essential for businesses to be able to react quickly and efficiently. The only question is how they can choose the optimal solution. Operational models and business needs actually determine the necessary BI solution’s advancement and maturity levels, its complexity and the value it can add.
Business and BI lifecycles
The way business leverages its BI solution can be thought of as a spectrum. At one end of the scale business treats the BI as a simple cost-center, one that adds no value to decision making, but rather serves as a trustworthy, standardized and timed report-generating machine that informs decision makers of events in the past. At the opposite end of the spectrum BI is an integral part of decision making by being implemented in business processes and making predictive analytics on the future. At this level BI informs the business of expectable events in the future, their predicted time of occurrence, their likelihood, and the reasons behind their probabilities.
Ideally there should be a strong relationship between a company’s maturity and the complexity level of the BI solution it uses. The advancement of business processes directly determines the requirements of the BI solution and therefore its optimal level of complexity as well. In case of a well-thought-out operational model the complexity of the used BI software is in level with the company’s data maturity, and the two can advance and evolve in harmony.
This path to maturity can be characterized as the different stages of the human life. As people grow and get more mature, they make more and more complex decisions and use more detailed tools and deeper knowledge.
Static reports, simple BI solutions
The moment a company is founded it starts using certain analytics tools but as it evolves its BI needs get more and more complex. Part of the natural advancement process is the implementation of new functions into the ordinary processes. In the early stages of data-maturity the simpler, static statements that are mostly used on the operational level predominate the pool of analytical tools.
Advancement of data-maturity
Even the simplest analytics indicate room for improvement in BI. Finding just one questionable observation within a simple statement is enough to get decision makers start thinking about implementing reporting technologies that output more precise analytics with more insight, in better format. As BI improves decision makers get a much better picture of their company’s current state. Meanwhile their need to get more information increases. In addition to the usual large summarizing tables, a need emerges for ad-hoc analyses focusing on leveraging data from particular business areas. Later appears the need for interactive dashboards for example for monitoring performance metrics. Demand for interactivity occurs in line with the growing number of reports, as decisionmakers’ familiarization with large amounts of data is quite expensive, and the creation of ad-hoc statements also comes with quite the time and resource needs.
Complex BI platforms
One milestone in a company’s path to data-maturity is the point where it decides how to tackle the growing need to generate more reports. Going back to the comparison between stages of human life and business maturity, this would be the end of youth and the beginning of adulthood, where businesses’ decisions have a long-term impact on their future.
At this point, not only the direction of a company’s path to maturity is formed, but rather how efficiently it can start and finish the journey. Sometimes it’s sufficient for businesses to stay at lower levels of data-maturity, such as leveraging static reports. Eventually the ultimate goals are value creation and staying competent on the market, for which a BI platform is only one tool and when the size and ways of conduct allow it, those goals are just as easily achievable with simple analytics software. However, as data is growing and competition evolves, it becomes quite difficult to maintain success with those primitive solutions.
The numerous amounts of options and the inherent investment costs make the steps of getting to the next level in terms of BI complexity rather difficult. When choosing between possible BI solutions, thorough financial analyses need to be carried out in order to get the highest possible return on investment. However considering only financial aspects is irrational during this decision as BI development may draw many additional advantages, as making it possible for businesses to monitor and evaluate strategic performance. In addition to evaluation, a well-chosen and implemented BI technology can help shape the business and lead it on the optimal path towards success.
Business analytics platforms can serve companies long-term and become an integral part of the operation if high levels of data-maturity are complemented with complex BI solutions. Success is unimaginable without making data-based and data-driven decisions.
How to decide on a BI solution?
Keeping data-maturity and BI complexity in sync is only one of many things that need to be considered when developing and reforming data analytics platforms. What does one choose? Simple Excel reports, complex ERP systems, data warehouses or interactive dashboards? Usually these solutions just co-exist, in lucky cases they can complement each other, in worse scenarios they serve business needs redundantly. Even if the desired BI path is determined and is in sync with data-maturity, companies have loads of other aspects to take into account before implementation. For the sake of an example, in case of committing to certain dashboards, companies better create a checklist.
Along the path to data-maturity companies can go from using static reports to leveraging data-based practical solutions, or even to automated decision making. Return ratios calculated for BI solutions help efficient business development and tool selection. Decision makers are advised to evaluate their current BI solutions on a high level from time to time, because growing data, technological advancement and data-maturity together determine the next optimal BI solution.
Works cited:
- TDWI BI Maturity Model
- https://www.dundas.com/resources/blogs/top-7-things-to-consider-when-choosing-a-bi-tool
- https://www.holistics.io/blog/3-things-to-consider-when-choosing-the-right-bi-tool/
Authors:
Richárd Pertics – Business Alanyst
Kristóf Rábay – Data Scientist