With data fast becoming a gold mine and providing multiple opportunities for growth, it is crucial that organizations learn to leverage it effectively. Therefore, it is crucial that they keep themselves updated with the latest trends in the data and the analytics industry.
The rapid adoption of digital transformation initiatives across multiple industries has witnessed explosive growth in data. While the data has become complex and challenging to handle, if used with the right tools, it can become a gold mine for organizations; opening multiple windows of opportunities.
As per a 2021 report from Allied Market Research, titled “Big Data Analytics in Retail Market Insights – 2027,” the global data analytics market is expected to reach US $ 25 billion by 2028, registering a CAGR of 23.1% from 2021 to 2028. Organizations realizing its immense potential are reinventing themselves both culturally as well as technologically to utilize the data to its fullest and become digital-first.
Here are the top four data and analytics trends that IT leaders should keep an eye on in 2022:
Incorporation of blockchain will increase security in data
Blockchain technology has already become popular for ensuring secured transactions with minimum effort. With no need to rely on third-party, transactions are approved by the network of peers.
ECommerce organizations have begun to use blockchain for their online transactions. The technology has also found its place in other domains, including healthcare and security. Also, with the emergence of the Internet-of-Things (IoT), the volume of data is increasingly becoming exponential creating a potential threat to its security. Blockchain technology, thus, will be a better option as it is known to be tamper-proof and hacker-proof. Moreover, blockchain can deliver anonymized digital identities to IoT devices that enable sharing data between them.
Greener Data Centers
Data centers have gained a bad reputation for carbon emissions as well as the amount of energy they consume. As organizations strive to reduce their carbon footprint, they will turn to ‘model efficiency’ that describes simple, efficient AI models to solve complex problems using fewer resources.
This means that AI models are not required to have as many layers in their neural network. Organizations will increasingly look for AI models such as Bayesian to solve complex problems using less computing power, less data and overall training.
Machine learning ops will drive AI adoption
While a surge in computing power, as well as massive data sets, have led to ease of creating AI models, the hard pill to swallow is that not many AI Proof of Concepts reach production. Not only that, but even fewer AI models are able to deliver the intended, measurable business value.
AI Engineering or MLops is emerging as a dominant trend for defining the best practices as well as processes to take machine learning models to production while simultaneously operationalizing them in real-world contexts. In the near future, the improvement in MLops will lead organizations to build stable machine learning models with application-level quality.
Data analytics will be responsible for business resiliency
Organizations are increasingly focusing on operational agility as well as resilience to deal with adversity, and analytics has become a driving force for it. Organizations specifically in the supply chain industry plan to invest in stability and agility in the next couple of years.
Gathering raw materials from various geographical locations is one the biggest hurdles that today’s organizations face, making a strong case for implementing procurement analytics and forecasting a critical component. It helps them analyze potential risks in the supply chain and effectively assist in responding to internal and external obstacles while increasing transparency and insights for better decision-making.