Insights that deliver: Closing the gap between data and its business value

Not all insights are created equal

Today, the cost of leveraging predictive technologies like AI and ML is going down. In an economy of insights, as the cost of prediction falls, the value of a company’s data goes up. We can generalize this for understanding where the RoI on AI-powered insights comes from. Today, the cost of analytics toolsets and insight engines is lower than it was years ago. Yet, the dividends on data-driven insights are higher than ever before. This disparity between the reducing cost of prediction, analysis, and AI in general, along with the rising value of data insights in propelling businesses toward growth, alludes to the fact that not all insights are born equal.

Insights are insights only so long as they drive business value - otherwise, they are mere pieces of information that bear little relevance to the business itself. This means that insights are the bridge between data and its potential value for the business. To close this gap, enterprises must adopt a framework that addresses the attenuations across the entire data life cycle and powers insights that truly deliver value to the underlying business. How is this possible, and what are the steps to getting there? Going deeper into the business of data

A data point in the CRM can tell us that advertising a durable product to a customer who has already bought it, is of no consequence.

While data propels businesses in today’s digital-driven world, it is ultimately business processes that result in creating all that data. However, this data is merely an as-is representation of the underlying processes. Data, in such a state, is of no consequence to the bottom line of the business. In this state, data is neither sanitized for the use of insights generation nor is it available to be pivoted for contextualization. For example, a data point in the CRM can tell us that advertising a durable product to a customer who has already bought it is of no consequence. This is an insight that generates business value, makes marketing more efficient, improves the results of the ad spend and boosts customer experience.

Moreover, that same data point can be leveraged by post-sales representatives to improve the quality of service, while product teams can contextualize customer feedback with the amount of time they have spent with the product. The same data point is locked away in the CRM, unlocking unique insights to various teams. This points to the need for anchoring the use cases of data to the bedrock of business value, and this philosophy must be sustained in the what, why, and how of building business value-driven insights that bring a positive impact on the bottom line. So, what does such a philosophy look like in action?

Getting insights to deliver: Four key steps

To enable better decisioning and improve outcomes with contextual data insights, enterprises must forward their transformation strategy along with this four-step framework:
 

1. Data maturity: While organizations have transitioned to digital operations, the data generated from their digital systems is seldom of use as it is - just as that data point in the CRM that we had talked about. Before we can leverage data within analytics workflows, it needs to be taken along a comprehensive quality assessment filter where it is checked for cleanliness, completeness, inconsistencies, and conflicts. Moreover, data pipelines must be able to capture business and technical metadata in line with data quality (DQ) rules while effecting a consumption mindset and facilitating the creation and availability of data catalogs and lineage. This enables monitoring of the data ecosystem and makes available trusted datasets that teams can then leverage to build their use cases.
2. Data platforms: While enterprises mostly focused on a data warehousing and data lakes approach until now, leaders are increasingly discovering the benefits of bringing modularity to the data architecture. This means that new approaches to data management leverage a data fabric, which relies on domain-specific teams to manage the data and serve it as a product within the enterprise. Carried out on an enterprise-wide level, a data fabric builds access to high-quality data, incorporating data maturity principles up to the point of consumption for building analytics use-cases. The data fabric also builds security and compliance into data products from the ground up — internalize masking, encryption, and tokenization, and enforce access permissions in line with data residency and data flow constraints of legislation underlying the geographies in question.
3. Twin Ops: Leveraging observability at scale, the objective of Twin Ops is to reduce the time-to-market for analytics use cases by building a cohesive pipeline for developers where data orchestration and validation, model training, performance engineering, and data literacy have been unified within a single workflow. By aligning DataOps and MLOps within a single platform, enterprises can keep their history of learning from data intact, even as the AI and ML engineering roles are occupied by new people over time.
4. An AI mindset: Finally, contextual insights are built on top of context-rich data. The dividends on data-driven insights are fully realized when the data has been examined and analyzed from all angles. For instance, by leveraging the domain expertise of marketing, accounts, customer success, and product teams, the data catalogs can be enriched to enable consumption-based views depending on the process that a use case intends to improve. Such a consumption-based ecosystem enables the enterprise to fully embody an AI mindset, where proof of concepts (POCs) can be rapidly sped to prototypes. The objective of enabling consumption-based views is to build insights that are context-aware and serve the logic of the vertical in question.

With this 4-step framework, enterprises can bring data and analytics together in a data perating model, and achieve a top-down alignment of business vision with use case design and implementation. Because a data operating model is driven by multiple resources and effective governance, leveraging the experience and resources of a technology leader is the key to bridging the gaps, leading with a viable vision for scaling successful use cases, and prioritizing promising use cases.

Moving forward

In such a competitive landscape, adopting a framework that enables businesses to unlock the full value of their data in an efficient, agile, and cost-effective manner is critical to succeeding with AI, ML, and, more importantly, with data. Only a small fraction of organizations have accomplished this feat on their own. Instead of going at it alone and hitting the AI winter, businesses must partner with resilient digital natives that have repeatedly helped their clients achieve success by empowering their organizations in finding insights in data that deliver.