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Codifying A Few Big Data Trends

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Rajesh Khurana, CIO & Head IT Infra, Societe Generale, India

A techno functional IT leader, Rajesh has two decades of extensive experience with captive service based & product organizations and also in application production support,legacy management & transformation, devOps,project & delivery management, data warehousing & business intelligence.

Data is always a critical and important contribution for the Business growth, strategy and roadmap, but in recent years, big data management and analytics become more strategic and on the fly with digital transformation initiative, each business of any type & size is leveraging the data in this fast-changing competitive world. With recent pandemic of COVID-19, business now very clear that without better utilize of data they can't survive and with more digital transformation, threat of cyber security also increased for which not only utilization, but data governance is very important. With all these requirements, it's changing how we collect, manage & utilize fast growing volume of data which is gold mine and most asset for any organization. On the basis, here's a Big Data trends we should keep an eye.

Data Fabric Implementation
The data fabric concept has been around for a few years, but it's become more favored now as data is increasingly scattered across hybridcloud/multicloud networks. Data fabrics weave together data from internal and external data sources to create data networks to power business applications, AI and analytics, as it makes your data machine understandable for AI/ML. This year we will see significant growth and interest in data fabric solutions as companies seek to leverage a common management layer to accelerate analytics migration to the cloud, ensure security and governance, and quickly deliver business value by supporting realtime, trusted data across hybrid-multicloud all in driving digital transformation.

Use of DataSecOps Tools & Technologies
Thanks to new technology, innovations and framework, in the last decade we enjoyed a great experience especially around the applications with accelerated development using Agile, DevOps & CI/CD. However, this created risk for datasecurity. Companies that were used to a more controlled software development life cycle had to adjust their security as well to the new continuous and agile deployment. Same thing happened to the data; it's moving to the cloud. An important driving factor in data usage is that more people within the organization are using data in what's called data democratization. Once access to data was limited to specific teams like BI or otherwise, it was very limited. And with data democratization, more teams are consuming more data. For example, sales, product, customer care, and other teams are

now actively seeking new datawithin the organization or outside of the organization to help them achieve their business goals. This means there are both small data consumers, but also more producers, and data that is continuously changing and it's much more agile in nature. This adoption of DevOps & DataOps leads to the methodology Data SecOps which is an agile, holistic, security embedded approach to coordinating of the ever-changing data and its users, aimed at delivering quick data to value while keeping data private, safe and well governed.

Artificial intelligence &machine learning will help organizations from various domains, including finance, marketing, healthcare, and so on will improve their performance to a great extent by increasing their efficiency and profitability while simultaneously reducing costs involved


Predictive Analysis
In early 2000, we have seen a lot of demand of Business Intelligence tools because of easy analysis and reporting. Thanks to the social networking, online shopping and more technology revolution, companies are swimming in the data that resides at various platforms. Predictive analytics is the use of past & current data to predict the future events & trends. This is associated with Big data, data science & machine learning. It's not only provided insight but also suggest the best course of action based on the identified trend & insight e.g retail business using predictive analysis which is further used to recommend personalized offers to customers by analyzing their browsing, shopping & buying pattern.

Blockchain & AI/ML
Blockchain & AI/ML are another trending technologies added to the list of trends of Big data. Gartner predicts last year that by 2022, AI will be embedded in nearly 60 percent of big data and analytics solutions. Blockchain technology was initially used only for digital currency e.g bitcoin but now being explored by banks for international payments & other transactions as it provide fast, secure & low cost international payment processing service with the use of encrypted distributed ledgers that provide trusted realtime verification of transactions without the need for intermediaries such as correspondent banks and clearing houses.

With the rise of the Internet, the volume of data is increasing, thus creating a potential threat to its security too. Blockchain is a good solution for this problem as it is considered to be tamper-proof and hacker-proof. In addition, blockchain can also provide anonymized digital identities to Internet of things (IoT) devices and enable sharing data between them. Artificial Intelligence& Machine Learning will help organizations from various domains, including finance, marketing, healthcare, and so on will improve their performance to a great extent by increasing their efficiency and profitability while simultaneously reducing costs involved. Integration of AI with big data analysis increase the accuracy of data which help the business to make decision by uncovering the pattern & insights that would be otherwise be hidden.

Data Governance Platform
The data market is growing dramatically and will continue for next several years. Keeping data secure and compliant is an ongoing priority. Before the Big data era, data managers were looking only limited scope in the data governance e.g data catalogs, data lineage, data quality, data access control, data security, master data management etc. Now with increased volume of data, Data Governance scope is totally changed, and it should include integrity, privacy, security, availability & usability. Big data is just reality and it's no longer a new trend, and because of that, the approach to Data Governance is shifting and lot data governance products are already in the market.

Conclusion
We are moving to a new economy, Data Economy. Big Data is one of the technologies with predictive analyses, high performing information system, AI/ML which changed the life of business and end-customer. We have to keep in mind that with large sources & volume of data, there is always threat on data privacy & security so Data Governance/Information & Cyber Security will continue be the top and essential trend in the market.

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