The Case for Vertical Integration in Analytics Platforms
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.
Enhanced analytics agility: Vertical trumps horizontal integration
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.
Boost time-to-insight with vertical integration and realigned processes
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.
Data democratization needs free flow of data and transparency around usage
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.
Better usability and tight integration propel effectiveness, speed and efficiency
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.
- Analyze the potential for speeding up analytics and promoting it in business units. Vertically integrated analytics tools empower business users and developers to be more efficient and effective. Quick and intuitive data preparation, analysis and presentation are key in analytics.
- Only in tightly integrated software can experts refine, reshape and enrich their data and present actionable insights in the most suitable way. And with speed comes relevance and effectiveness.
- Identify clusters of requirements that can be covered within a unified platform. Be aware that additional tools must provide clear benefits and must fit into the architecture smoothly. They must not generate additional isolated data silos that limit the innovative power of analytics. Open interfaces and metadata exchange are the technological enablers for the required transparency that the organizational framework must follow.
- Define principles that guide collaboration between units and the BICC in a decentralized analytics environment. These principles must consider the paradigm enabled by vertically integrated analytics software. By dividing work along the data flow, huge benefits in speed and agility can be realized to improve a company’s innovative capacity.
- Reassign and upskill dedicated developers in the analytics organization to train, coach and advise users in business units and local entities to master their own analytics challenges. Together with curating data for shared use and providing guidance through best practices, this creates the foundation for evolving a cost center into a successful service shop. While business analysts and data scientists move to center stage in decentralized analytics, the contribution of developers and data engineers to smooth operations cannot be overestimated.
- Extend the reach of analytics with user-friendly business software for predictive and advanced analytics and machine learning that includes leading data preparation facilities. Consider requirements of automating decisions even though they may not be crystal clear yet. Operationalizing, deploying and monitoring analytics and ML models in production will be relevant for all companies sooner or later and many are not prepared properly yet.
- Catalog all the analytics assets created throughout your organization, regardless of the tools used or the departments that created them. A comprehensive overview of reports, dashboards, data sets and analytics models is the oil to get the engine of your analytics processes running smoothly.
- Appreciate the potential of integrated software with exhaustive metadata collection. These tools deliver common and extensible semantic models, collect compelling usage statistics and provide transparency into where data is sourced from, how it is transformed and where it is presented.
- Consider cloud-based analytics solutions when selecting vertically integrated software. Software as a service combines the advantages of practically unlimited scalability, quick set up and resource-efficient operation. For modern analytics, the cloud is a platform that can deliver on today’s and tomorrow’s needs.