4 tech predictions for 2022: Industrial data management enters the 2.0 era

The past two years have been eventful for enterprises in the [Kingdom of Saudi Arabia/Middle East], with even the most conservative new technology adopter organizations being forced to take a digital leap that certainly would have been unimaginable had it not been for the unprecedented global pandemic acting as catalyst for digital-by-necessity.

In this year’s tech predictions, we are focusing on a very specific field of technology: industrial data management. More specifically still, we are zooming in on how cloud data storage will finally be connected to industrial use case solving at scale, unlocked by advances in what has long been an elusive yet critical “hidden layer” between data storage and application logic. Before we start, two notes to inform readers:

  1. For those looking for more general technology predictions, there is no shortage of well-researched examples published around this time of the year by the likes of IDC, Forrester, and Gartner as examples; and
  2. This is not another vague prediction on how digital twins will transform industry (there is equally no shortage of great research and predictions on this topic dating back a good decade at the convenience of any web search engine)

Augmentation, democratization, and consumerization

Searching for good terms to categorize and summarize our four industrial data management tech niche predictions for 2022, we surprisingly quickly arrived at augmentation, democratization, and consumerization. Enough prologue. Please find below our predictions for industrial data management tech niche’s strategic technology trends that we believe will drive significant disruption and opportunity over the next three years—if not even sooner.

1. Corporate data governance gives way to democratized ambient data governance

Organizations need to be able to apply different styles of governance for different types of data and analytics. Data governance topics such as data quality and data lineage transparency move from being a centralized, use case agnostic practice; to becoming intrinsically linked to actual data consumers and their data-driven use case requirements. Through this paradigm shift, data governance—and master data management—is no longer a roadblock (or tax) on faster use case execution and greater use case development team autonomy, but becomes infused into use case solving itself. As a first step, we predict that data discovery will become an indistinguishable part of data governance.

2. AI teaches ET/OT/IT data to speak human

AI-driven active metadata creation permeates industrial data management, shifting the emphasis from data storage and cataloging to a true human data discovery experiences. For application developers and data scientists, understanding—and handling—industrial data is not as straightforward as dealing with most tabular data.
What is even more difficult (for all who are not SMEs with years of intimate experience with the asset) is understanding the context of, as well as further contextually related, industrial data. Using NLP, OCR, computer vision, trained ontologies and graph data models, ET/OT/IT will be automatically contextualized for intuitive human as well as programmatic discovery and analysis.

3. Enterprises will invest more in metadata (and its management) than in data (and its management) itself

Data science teams are scrambling to convert the contents of their data warehouses and data lakes into business value (not that DS is the only data consumer class by far, but they are the ones with the highest expectations to deliver transformative solutions.)

Data lakes have become data swamps. To fulfill the market’s demand for human understandable data, despite an increasingly complex data landscape, metadata management is stepping up, driven by the expansion of Chief Data Officers’ (CDO) mandate to include metadata ownership (from the CIO).

4. Data operations – or DataOps – will connect data managers to data consumers in real-time, at unprecedented scale

DataOps is collaborative data management for the AI era. The convergence of data management with data analytics continues to accelerate, driven by an exponential growth in data literacy aspiring data consumers. Seamless data operationalization across all workflow steps ranging from data sources to live applications becomes the new “data trapped in source system siloes” challenge for digital and innovation leaders.

CNTXT is your digital transformation partner in the MENA region. Through leading Google Cloud solutions, industrial software, and digital know-how, we enable you to act on the industrial data trends to maximize your enterprise’s competitive advantage.

CNTXT can help you future-proof your data infrastructure, enabling you to contextualize your data so it’s ready for numerous AI applications and to capture the insights you need.

Together, we can unlock your enterprise’s limitless capacity for growth through digital transformation and data-driven insights. To book a consultation, please contact us.