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Let’s Get Data Tastic in 2022

Blog: Jim Sinur

It would be easy to focus on the data challenges facing organizations today and respond reactively to them. However, we all see the problem of massive amounts of data coming in faster than we can deal with it. We are all learning how to cope, but I think 2022 is the year organizations make some headway on getting ahead of this problem and start making new opportunities for themselves. I want to enumerate some practical things we can do in this post. One is to work towards building a better data foundation by augmenting and modernizing towards an authentic data fabric/mesh, and another is enabling automated learning to find the data nuggets that are candidates to leverage and finally looking at new ways to gain business advantage by leveraging data.

Build, Augment and Modernize

All organizations can make their data resources better. The opportunity here is immediate and multi-faceted as organizations build toward data fabrics and meshes. The core of a proper data fabric would be a unified database that can handle multiple different data styles of operations, including transactional and analytical with the same data. It will help reduce the number of copies of data necessary for different kinds of operations. A database that can work efficiently and seamlessly with hybrid and multi-cloud situations is a minimum price of admission these days. A database that can mix real-time data with archival data is a must. It requires organizations to move from specialty databases on-prem to a real-time catalog-driven approach that finds and data with different speeds, formats, and data types and leverages that data. All of this is for better decisions and better handling of emergent situations while dealing with limitless speeds/feeds. This technology exists and is growing more capable of supporting an actual data fabric/mesh needs to cope with businesses.

Automated Learning

With the recent strong growth of AI and Data Mining, organizations are tasting some of the early benefits of learning from data leveraging emerging digital technologies. Getting a handle on this automated learning will be table stakes for survival going forward. Even armed with algorithms, people can’t keep up with the influx, speed, and variety of new data. Trained machine learning algorithms can understand many data channels such as email, chat, speech, and image sources. This kind of AI, combined with computational statistics, can help find opportunities or threats in the future. Data mining can explore data leveraging unsupervised learning to visualize and provide patterns of interest. Advanced data sifting can employ neural networks to find opportunities in the tidal wave of data.

Gaining Business Advantage

I think organizations have done a great job of using data to gather leads and close sales to increase the odds of growing revenue. Still, there are more opportunities beyond micromanaging resources. While optimizing resources is still a top priority, organizations are encouraged to expand their view to include opportunities for automation, better interact with customers, use digital twin opportunities for better visibility, and leverage data to better manage emerging situations detected in existing or new data sources (voice, image, video, GPS, etc.). It is the more difficult area to move forward in as a leader because there will be some pioneering and risk unless your competition does it first to show the way.

Net; Net:

Organizations must have activity on all three of the above paths to better data utilization; however, the priorities will vary by organizational culture (risk-averse or not). For example, assertive organizations that want to capitalize on data will gain business advantage first. Still, most organizations will grow into an advantage from the bottom up by learning to leverage a new generation of databases inside a morphing data fabric/ mesh. In parallel, most organizations will be growing their capabilities in automated learning opportunities starting with low-hanging fruit.

Additional Reading:

Data-Intensive Applications

Reducing Data Sprawl

Data Infrastructure Debt

Unified Databases

Real-Time Data

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