Data trends that defined 2022 – a review - Anjna Bhati, Director - Data Analytics & AI, BluePi Consulting

In the current digital world, new digital experiences are sought after with technology enabling them constantly and ‘data’ driving the whole exercise.  Data science, Big Data Analytics, and others are supporting decision-making and data-focused product offerings.  Businesses have to rely on near real-time data to draw actionable insights for better outcomes.  Data Analytics is today enabling large enterprises, SMEs, and Startups to achieve operational excellence by optimizing processes, enhancing customer experiences, and enabling the implementation of an effective marketing strategy.

Some key developments in the data domain in the past year are summarized in the next few lines.

Modernizing legacy applications was the top priority

A significant number of organizations accelerated their digital transformation efforts to upgrade their business processes to deliver better customer experience, increase employee productivity and establish business reliability.  For automating content ingestion, hyper-automation technologies like Natural Language Technology, Optical Character Recognition, and Conversational AI were in demand.  These helped to reduce operational costs significantly.  Composability, the system design principle, that deals with the interrelationship of components, was leveraged by many enterprises.  It enabled high flexibility, agility, and improvement in time-to-market.  Duplicacies of applications and data were reduced and there was a continuous improvement too with it.  Business Process Automation solutions were used to automate multi-step business transactions and Cloud computing with open-source platforms accelerated the app modernization journey.

Data Lakes adoption for Big Data Storage

The unstoppable increase in data generation has led to organizations opting for data lakes more than before to store structured and unstructured data sets in their native format.  Data lake houses as primary storage of data became an economical option with the increase in remote and hybrid working.  The growing demand for cloud-enabled data platforms, the need for actionable business insights, and the higher speed for data retrieval drive the data lakes market. Furthermore, data manipulation is efficient and faster with data lakes which is necessary for the rise in remote and hybrid working environments.  Data Lakes also offers an open and secure platform as well.

The spotlight was on Data Engineering

As organizations aimed to increase their decision-making process, the demand for data engineering along with data analysts grew.  Similar to DevOps benefits for the development teams, DataOps helped data engineers.  Data Engineering teams developed storage and transport strategies for hybrid and multi-cloud as well as edge environments. 

Data engineering plays a crucial role to refined the processes of transforming, managing, and tracking the organization’s data for better analysis leading to high demand for data engineers.  The key sectors that leverage their services are Banking and Insurance, Retail, eCommerce and Internet, Energy and Industrialists, Pharma and Health, and Telecom among others.

Data quality challenges persisted

A significant number of organizations continued to face the issues of data quality and validation aspects which are key to any business performance.  Data quality challenges were seen in businesses throughout the year.  Data quality challenges include inaccurate data, outdated data, lack of standardization, duplicate and incomplete data, and more.  This was seen more in organizations that used low-maturity DataOps practices where users were unsure of the quality of data.  This can be addressed proactively and holistically with the appropriate strategies without causing a high impact on the organization’s products and services. The need for data quality systems and data regularization has given birth to companies that provide solutions for these challenges.  They offer services in checking for data discrepancies and performing integrity checks throughout the data Extract, Transform and Load (ETL) process.

The rise in the adoption of AI, ML, and Advanced Analytics

Organizations today are acknowledging the power of data and the value of data-driven decision-making than before.  Traditional analytics methods do not have the capabilities to analyze the massive amount of data that is generated every day.  An automated approach for data analysis at scale is required.  AI and ML systems can help to spot patterns, detect anomalies in larger data sets, and make accurate predictions through predictive analytics. With this, the organization’s business processes can be improved and optimized which was not possible earlier.  AI and ML help organizations to deliver customer support by leveraging intelligent chat bots and providing personalized interactions, without the support of additional customer service personnel.  Along with the implementation of a data lakes strategy, AI-enabled systems can gather and analyze huge amounts of customer and user data which improves decision-making.  Data visualization is also made possible with AI-enabled data analytics.  Decision Intelligence provides several options to deliver the advantages of eliminating biases, aiding data-driven decisions, and also increasing the speed of decision-making processes.  All organizations that leverage these technologies that deliver real value, succeed in their digital marketing efforts.

In addition to the above, businesses also began to pay more attention to data security, privacy, and governance aspects as well.  As the year 2022 saw more organizations adopt data-driven models to their business processes, the data analytics industry also witnessed huge growth and this trend is here to stay.  For all organizations who look at taking their business to the next level, undoubtedly ‘data’ is providing the opportunity.