The Five Priorities for Every Data Strategy Today

Earlier this year, I sharedthe top trendswe are seeing when it comes to solving enterprise data management problems. We continue to deploy and put these emerging solutions to the test (read more on that from our case studies) as we work with organizations to push for digital transformation. The volume and diversity of information continues its expansion and the age-old challenges of siloed and unreliable data continue to pose a threat to getting returns from these large data transformation investments.

This is causing many organizations to revisit their data strategies, taking a deliberate and objective look at their business through the lens of data, anticipating ongoing disruptions, and building in the agility required to support their data teams.

These are the common components I am seeing consistently arise as key ingredients of today’s most successful enterprise data strategies – especially critical to the success of organizations that are undergoing a digital or data transformation, cloud migration, or deploying new business offerings. 

Quality data enables the findability, discoverability, reusability, traceability, and the ease of governance of data to ultimately connect people with the knowledge, content, and individuals they need to take actions or make decisions. And good quality data begins with metadata. 

The complexities related to data quality and big data governance have primarily increased due to the silo between types of data – structured and unstructured. Metadata bridges this gap with its essential and expanded role of ensuring that data can be found, used, secured, and reused by the appropriate parties.

We define quality metadata as having the following characteristics of the “four Cs” — quality metadata is:

As a result, the modern data stack needs an approach to intuitively deliver and connect metadata to handle the dynamism and size of big data. A starting point for that is by having a strategy for cataloging and understanding enterprise data and leveraging active metadata as a fabric or abstraction layer to connect data without the need to move the physical location of the underlying data (a data fabric architecture). An enterprise data catalog is one foundational component of the solution and the pillar for a forward looking data strategy. Organizations with the need and use cases for higher flexibility and quality of aggregation of contextual metadata are bringing in data catalogs powered by graphs to properly integrate knowledge and data across the organization whereas traditional data catalogs are lacking flexibility to support new types of data sources as data continues to grow. 

A knowledge graph, a model for applying business context (semantics) and capturing relationships between your data and content, allows organizations to create a machine-readable version of their collective institutional knowledge. Knowledge graphs serve as an abstraction layer that aggregates data that is heavily dependent on context, connectivity and relationships – in the way people think about and describe data – even if it is siloed and in diverse formats. There are a few ways a knowledge graph helps support digital transformation and data management capabilities: 

Many organizations that we know to be successful at delivering advanced user experiences and are ahead in their AI journeys are those that have adopted a lean data management and governance strategy that derives meaning and value out of data for the enterprise and uses knowledge graph solutions to connect, organize, and standardize their data. 

We are seeing a rapid shift to cloud infrastructures for business and data processing. Over 80% of the organizations we are currently working with have some form of cloud migration or engineering efforts taking place.