The effective use of data and analytics is a challenge for most companies today. Too seldom do companies generate relevant insights as quickly as they desire and need to. Analytics content must be created in an iterative manner and it must not be impeded by the restrictions caused by data silos. The fast creation of reliable results and sharing them in a secure manner requires seamlessly integrated software that supports the complete analytics cycle from data ingestion to presentation.
Only a vertically integrated software delivers the required degree of flexibility for users through its end-to-end features. This is a clear shift from the paradigm promoting efficiency through horizontal integration. Creating all reports in a single tool and storing all data in a common data warehouse was meant to boost efficiency. Instead of curbing expenditure, horizontal integration curbed the innovative capacity of companies to use their data.
When BI started being industrialized in the 90s, it was largely considered an IT topic as it required fundamental technical expertise. As such, centralizing competence to provide stable service was the obvious move to make. By centralizing competence and standardizing tools, cost advantages were expected. What was lost along the way was the flexibility businesses required to freely visualize and analyze data in new ways that are fit for quick decision-making.
This brought about the rise of shadow BI, which was initially powered almost exclusively by Excel. Change came a decade later with the advent of user-friendly self-service BI and visual analytics. Early resistance was largely overcome when these tools were embraced as an opportunity to eradicate the bottlenecks created by cost-oriented BICCs. Unfortunately, a general lack of governing capabilities, originally perceived as guarantors of flexibility, and the dissemination of analytics into all corners of modern companies overstrained the approach.
To overcome the defects of earlier generations of analytics and business intelligence software, vertically integrated data and analytics software couples the flexibility required for quick insights with governance features for scaling decentralized self-service and blending it with central delivery. This technology has been available for some time now and has penetrated many areas. Various developments over time have combined to make them the powerhouses of companies successfully deploying analytics to unearth the value of their data treasures.
Mastery of data usage gives companies an edge over their competition. Converting their data into insights effectively throughout the organization enables them to support decision-making and drive innovation. To this end, a good share of companies sees huge potential in vertically integrated data and analytics software. While laggards have not fully bought into the benefits of the concept yet, leaders have already acted and are reaping substantial rewards.
Today, these tools are quicker to implement through the cloud and easier to use than ever thanks to ML-based augmented guidance features. Powered by these technological innovations, the increasing scope of analytics requirements is successfully covered by tailored solutions.
Advanced and predictive analytics, machine learning and AutoML are all prime examples of this increasing scope. While they are not yet supported by integrated data and analytics software as well as reporting and data preparation, they are among the top investment priorities for future implementations.
While the primary goal of horizontal solutions was to leverage synergies and cash in on economies of scale by serving the whole company, vertical solutions should increase agility. And in a dynamic world, flow is more important than scale. Therefore, it is not surprising that integrated data and analytics software is not widely considered as a tool for serving the whole company.
As with any tool following new paradigms, you must realign analytics processes, roles and responsibilities. This affects requirements and implementation processes as well as the responsibilities of dedicated developers at the center of analytics gravity. Increasing data literacy and intuitive tools empower business users. But to deliver reliable results, they need decent service from full-time experts.
There will always be room for improvement in the analytics process as demand and expectations rise constantly. And transparency is a must to democratize access to data in a company. Having comprehensive metadata providing a view on data lineage shows where data is used. Knowing where it is used and who uses it makes it easier to agree to share it.
A major threat to the free flow of data and ideas are data silos, more often created by restrictive access policies than by incompatible software. With vertically integrated software, one has to be very cautious not to create more of them. Cataloging all available analytical assets helps to lower barriers by making visible what others achieve with data.
Companies realize various benefits when deploying integrated data and analytics software. The clear number one benefit for companies of all sizes is better usability through unified interfaces, integrated metadata and augmented analytics features – but that is only a means to an end.
It is an enabler to engage more business users to provide more relevant results in less time to inform and automate decisions. A look at the advantages of deploying integrated data and analytics software shows the stark contrast in satisfaction. While vertically integrated software is no magic wand, the satisfaction with results and creation are twice as high on average.
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As a Market Research Analyst, Ann-Katrin is primarily responsible for the planning, coordination, implementation and follow-up of market research projects at BARC. She oversees the entire process of creating studies – from designing and programming questionnaires, conducting and analyzing surveys to the layout of studies and infographics on various topics. She also maintains the content of the BI Survey website.
