Unstructured data continues to remake the data management landscape at a time when there not only is an unprecedented amount of data being generated, but it’s also being collected, stored, processed and analyzed in multiple places (on premises, in the cloud and at the edge) and moved between those environments. Enterprises are using videos, images, IoT sensor data, social media and similar information as foundations for much of the analytics, machine learning and business intelligence tasks they perform. It won’t be a surprise to see unstructured data continue to be a focus of enterprises’ data management efforts as we roll into 2022.
So, what other data management trends can we expect in 2022? Here are a few:
IT leaders will concentrate on using the cloud to get value from unstructured data
IT leaders know the cloud is much more than a replacement for on-premises infrastructure. It is an elastic compute platform that organizations can leverage to deliver a competitive advantage and agility. We don’t know enough yet about how to leverage the cloud to analyze unstructured data. With the growing interest in machine learning and artificial intelligence (AI), we will see more investment in unstructured data analytics and data management solutions that enable this. Since unstructured data is very large and unwieldy and much of it is growing outside the cloud at the edge, data management that spans edge-to-cloud and simplifies ingestion of unstructured data for cloud analytics will become a notable trend.
Unstructured data analytics workflow solutions will emerge
Processing and indexing petabytes of unstructured data is today largely a manual effort. Large organizations employ legions of data professionals to search, catalogue and move this data so it can be ingested by analytics tools and manipulated. There’s a dire need to simplify and automate these processes. Solutions that index files easily across multiple file and cloud silos and automate the systematic data movement will be on the rise. Also, data analytics solutions for unstructured data might be verticalized, so they are sector- or application-specific. For instance, medical images and how they are interpreted is a contextual event requiring specific knowledge of clinical data sets. Organizations are creating custom workflows consisting of cloud-based analytics tools like Amazon Comprehend for detecting personal identifiable information (PII) along with manual data movement and data lakes. The time is ripe for commercial data management solutions that can enable easy search of specific data sets across a global enterprise and stream this data continually to systematically automate the workflow of unstructured data analytics.
DEI will become more important to IT leaders as they expand their data management teams
Hiring a diverse workforce makes business sense given that more than half the graduating population is now female. In 2022, IT leaders will look to create a culture that is more conducive to equality at the grassroots level, such as by providing greater flexibility in remote work, recognizing diverse talent and fostering diverse role models both as managers and as team leaders.
‘Data Monetization’ and related strategies will be popular in 2022
The traditional concept of data monetization has revolved around mining CRM, ERP and other core business systems for intelligence on customer behavior, product demand and inventory trends. However, machine learning is a game-changing tool that relies upon unstructured data. Teaching a car to drive on its own needs data related to driving on roads with disparate surfaces and traffic lights patterns. If you want to improve satisfaction rates for customer support phone calls, you need to be able to analyze conversations. That’s why we are seeing companies like Snowflake announce support for unstructured data. They are providing data warehousing in the cloud and making it easy to answer those arbitrary and open-ended questions. The net trend is that monetization of data is shifting from a focus on structured data to unstructured data because that’s where much of the opportunity now lives for using data to improve customer relationships and revenues and to reduce risk and gain a competitive edge.
It’s time for IT to embrace data silos
Data silos are not going away, and nobody wants to commit to vendor lock-in to avoid the silos. The answer is to not worry about the silos but to seek solutions that can look across the data – search, classify, secure, visualize it in place – without forcing you to put all your data into one location or technology. Another area that will gain visibility is cross-platform, portable tag management. This would enable data managers and data scientists to move files into new clouds or applications yet retain the tags that are critical for rapidly searching and segmenting data to feed data analytics pipelines. The role of storage IT is also evolving to include data management and enable business outcomes rather than simply manage infrastructure.
Data management will continue to be a hot market for venture capitalists
Data management is driven by very strong tailwinds that should continue to bolster its market growth. The explosion of unstructured data, the rise of data at the edge and cloud, and the shift of data analytics to monetizing unstructured data are all massive forces behind data management relevance in the market. Venture capitalists see the success of companies like Snowflake and don’t want to miss out on the next big data management opportunity. Investors are always looking for the next big thing. How can you leverage the market trends to create a disproportionate advantage? A good way to approach this is to look for a data management problem that you understand well, which hasn’t yet been addressed by others, is pervasive some market segments and is solvable. Hot areas in data management include cloud data management, data management with analytics, data security and unstructured data management.
Unstructured data will be a key focus for enterprises in 2022, but in such a dynamic sector of the industry, it won’t be the only one. Enterprises will want to capitalize on the venture capital money that is coming into the space to expand their capabilities, monetize the newfound capabilities that come with machine learning and unstructured data and ensure their workforces are diverse enough to not only reflect society as a whole but take advantage of burgeoning talent in sectors of society that in the past had been relatively untapped.
Krishna Subramanian is the President, COO and co-founder of Komprise