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Data Management Technology: Trends & Challenges

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The last two years have been significant in the growth of Data Management technology, mainly because of the pandemic and associated factors. Almost overnight, large, medium, and small organizations located far and wide, suddenly realized the importance of online business models and hosted Data Management services. The coronavirus gave the final push to global enterprises to “adapt or perish.” The managed services offered by cloud providers have many advantages, one the most important of which is freeing up on-premise hardware, software, and employee resources for more critical work than routine enterprise Data Management.

Data Management Technology: Noticeable Trends During 2021-2022

Data Management Trends in 2022 highlights that increasingly, global organizations are moving to cloud-based Data Management platforms because of scalability. The cloud service providers offer fully managed, scalable, and economical Data Management services without any loss to Data Quality and security. Cloud platforms help businesses maintain centralized control of their data and easy access to the data on a need basis. 

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The significant DM trends continued from last year are:

  • Augmented Analytics: Augmented analytics has suddenly become very popular as it helps automate most of the data acquisition and data preparation tasks, so that human analysts can devote their time on deep drilling for insights. Premade ML models and Natural Language Processing (NLP) are the latest arsenal in the data analyst’s toolbox for improving the quality and reliability of analytics or BI tasks without depending on the expertise of data scientists. Augmented analytics also gives forward push to citizen data science.
  • Self-Service Data Management: Global businesses are going for “data democratization,” which will make ordinary sales, marketing, and customer services staff into self-supported data analysts. AI and machine learning and have together ushered in an era of self-service analytics and BI platforms and tools, which empower all business users to manage and analyze complex business data without expert help or training. The functional teams within a business can now operate hands-free on their daily data-processing tasks.
  • Cloud Analytics as a Service for Data Analytics: The cloud analytics service providers have grown to an impressive position in 2021 and will continue to grow throughout 2022. Cloud analytics offer several advantages over on-premise analytics, the most important of which is freeing up in-house Data Science teams for more critical technical projects. The second most important advantage is that cloud analytics helps to free up in-house resources and routine maintenance time. These two reasons jointly offer huge cost reductions, increased efficiency, and optimized business processes. As cloud technology continues to improve, it will soon exceed an average business’s performance goals. According to Gartner’s prediction, 90% of solutions in the data analytics field will be based on cloud analytics in 2022.
  • The Chief Data Officer (CDO): Mainly a strategic role, the CDO reports directly to the Chief Executive Officer (CEO) of an organization and wields great power in shaping the Data Management strategies, policies, and processes within the enterprise. Smaller organizations may not yet have realized the worth of a CDO, but large enterprises have certainly subscribed to the idea of hiring and empowering a CDO to spearhead the Data Management function. The CDOs assume responsibility for anything and everything related to enterprise data and Data Management — from aligning overall enterprise goals with Data Management goals to overseeing Data Governance matters. Data Governance, along with DG tools, once again establishes the power of automated services in mitigating the risks associated with data privacy laws and regulations.  
  • Data Exchanges: Data exchanges have grown in the recent years as a way to boost business revenue and nurture collaboration between partner businesses. Organizations are either exchanging data directly between one another or engaging in intermediary service providers. The healthy collaboration between exchange partners has many competitive advantages, the primary one being sharing invaluable insights for mutual gain.
  • Blockchain as a Replacement for Traditional Databases: Organizations are slowly picking up the trend of replacing their legacy databases with distributed ledger systems. This new technology offers security, asset tracking, smart contracts, and audit trails. However, the unique characteristic of this technology is that after storing a transaction in blockchain, one cannot alter it.
  • Data Stories Bid Goodbye to Bland Data Visualization: The interactive data stories that started gaining steam in 2021 will continue to dominate the executive meeting rooms in 2022. Data stories put data in context and develop a strong narrative around the displayed data — explaining both the “hows” and “whys” of data-driven insights.
  • Growth of Knowledge Graphs: Knowledge graphs, containing two distinct layers, store the data on one layer (graph database), and extract data-driven insights from another layer. This technology is being extensively used by business leaders like Facebook, Google, and Twitter for evaluating customers, making business decisions, and developing products. Graph databases were used during the pandemic to model the spread of coronavirus.

A highly favored trend this year will be cloud-native services, making data analysis affordable and accessible among all business users. Modern Data Management platforms and tools will help analysts extract insights from available data without having extensive Data Science or software engineering knowledge. In a way, you can welcome this trend as one promoting Data Management for all.

As cloud platforms continue to storm the 2022 business landscape, there are three critical questions that business leaders and operators should be asking before selecting a suitable DM platform offering:

  1. Can the cloud vendor assure data privacy and security?
  2. How scalable is the vendor’s platform?
  3. Is the cloud platform future proof in terms of new technology integration and cross-platform interoperability?

Here are more Data Management technology trends from Datamation.

The three prominent trends that have recently entered the Data Management software market include the sudden rise of hybrid and multi-cloud data platforms during the pandemic, emergence of AI- and ML-powered Data Management and analytics platforms with automated tools, and the preference for the data fabric approach to Data Management as it allows data from disparate sources to be merged and unified with relative ease.

Data Management Technology: Challenges & Probable Solutions

The author of a Forbes Council Post remarked:

“Effective Data Management will index your data in context. Instead of searching for data, you’ll be able to stop searching and start finding. Effective Data Management allows you to store data with intention and escape the digital pack-rat habit. As the volumes of data in storage continue their rapid ascent, can you afford to ignore Data Management?”

If one carefully analyzes the two quoted passages above, it becomes clear why technology-enabled Data Management is becoming crucial for operational efficiency and overall business success. However, enterprise Data Management comes with its share of challenges.

Here are some Data Management challenges and probable solutions to consider:

  • Data Systems: In any enterprise, simply implementing data systems and processes does not solve any problem unless all business users become aware of the purpose, use, and benefits of such systems and processes. As a solution, core Data Management teams can be tasked with the responsibility of spreading data awareness and communicating the long-range benefits of data technologies to the average business staff. This is also an aspect of the data literacy strategy of a business.
  • Data Volumes: The ever-growing data volume is such a big challenge that all emerging data systems, technologies, tools, and processes have to be adapted to accommodate the unstoppable data volumes. Big Data as a Platform (BDaP) or specialized apps to integrate big data may just be a couple of the solutions in near sight.
  • Data as a Service (DaaS): Although this new and novel service approach promises lucrative revenue channels for a data-heavy organization, the most common challenges associated with this service model are privacy and ethics issues, but fortunately, many organizations have already taken on this challenge by hiring a chief data officer (CDO) to mitigate the risks.
  • Sharing Data Between Different Functional Teams: Single-source data platforms are the need of the hour to enable distribution and sharing of “same version of truth” (trusted) between different functional teams for analytics or BI.
  • High Volumes of Semi or Unstructured Data Acquired Through Various Real-Time Data Channels: The unstructured data have to be cleaned and prepared before they can be used for any meaningful work. Currently, Data Management teams may be working with third-party service providers for extended data services across on-premises, hybrid, or public cloud setups. Here are 10 big data challenges for Data Management teams, which highlight problems related to the volume, scale, integration, and security issues of big data.
  • Lack of Knowledge: Most people do not understand the uses of blockchain technology, which poses a major threat to its widespread adoption. Blockchain, with its inherent benefits of being immutable, auditable, and secure needs to be adopted as a mainstream DM technology.

Data Management Challenges in the Data and AI First Era

On one hand, organizations are keen on jumping to cloud platforms for their Data Management efforts; on the other hand, many of these businesses are struggling to adapt their legacy systems and processes to the latest data-technologies solutions. Still staggering under archaic data infrastructures, many of these organizations are not only ill-prepared to make a transition to modern AI-enabled platforms, but they are also not knowledgeable enough to take the giant technology leap. A BARC survey reports that businesses feel they are feeling the pinch of “skill gap present” on the market.

Finally, Peter Aiken in the webinar Data Management Best Practices, raises some important questions around existing DM practices and offers some helpful solutions. Peter Aiken is an expert on Data Management, and an associate professor at Virginia Commonwealth University.

Image used under license from Shutterstock.com

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