From Big data touching the lives of educators, to creating new data-based business models it has revamped itself dynamically and grown exponentially over the past few years. Its evolution shows no sign of stagnating as we are advancing to an era of AI based automation and computing. Here is how big data is gearing up itself for 2020 onwards. IBM is taking gigantic strides post IBM Developer Day 2018 with respect to Big Data and as an IBM Global Training partner here are a few facts about Big Data and its usage for 2020.
While a recent Research predicts that digital universe will grow to an incredible 163 zettabytes, (or roughly 163 trillion gigabytes) of data by 2025. For our reference 1 zettabyte = almost roughly 2 billion years worth of music which is enough to power you through a pretty long work week. We can apply this raw data across a variety of different concepts like creating custom learning models for students or offering better personalized healthcare or in retail analytics. But there still lies a considerable amount of uncertainty wrapped around both the analysis and the deconstruction of big data.
The prejudice however lies in the lack of skilled staff to a large extent where we are trying to address via ad-hoc solutions as per verticals as suggested by Mr. Joseph Jayakumar, Director Amstar Technologies. While we agree that much of the apprehension or perception behind the use of big data arises from a fact that nearly 80 percent of data is in an unstructured formats like audio, video, and social media and gets difficult to maintain and is a cost driven process to analyze in silos and by large is being generated much faster than we can feasibly keep up pace with.
Big data is predicted to evolve considerably in next few years and many businesses have started with planning and implementation of big data into their enterprises. As technology continues to grow we can start to envisage its impact on our lives before the dawn of 2020 from a strategic use perspective as highlighted below.
- Data science will soar
While there is no denying the fact that data shall be the new currency that will power our economy moving forward. We are well equipped with this and have significantly trained data scientists who are continuing to drive the future through exceptional daily innovations at work. It is critical that businesses have to start planning for the integration of data scientists into their organizational structure. This provides more opportunities for workers to explore this field. Infact data science has become one of the most rapidly progressing and promising field in IT thanks to the crucial role it plays in understanding Big Data. Also as Data has staying power and is not going away soon. The role of a data scientist will cease to be a specialized position that people hire for. Most data scientists will no longer have to think about distributed systems like Hadoop, Spark, or HPCs and Old technologies like traditional relational databases will catch up in performance and capabilities to these technologies which makes the need for Big Data a more viable opportunity in the future. In fact a recent report by IBM, The Quant Crunch has estimated that up to 2.72 million jobs will require big data science skills for 2020 onwards.
- Big Data will be easily Accessible
By 2020, Big Data will become more accessible. A key challenge for many enterprises will be unifying all of this data by definition. This is a big job if a skilled think tank team is not there. While Building data pools and other flexible storage environments is a major priority in 2019, we predict that by 2020, much of this critical data will be housed in systems that are much more accessible by the tools that will use them. This opens up limitless possibilities for business operations to be a purely data-driven activity. While we see landscape for big data evolve from highly technical, expensive model to a self-service pay for model where you are only charged for what you use, collecting and processing big data will be insufficient if business decision-makers aren’t able to process data or struggle to find value in it.
The reality in fact is that in today’s landscape to analyze big data we need massive infrastructure to capture, catalogue, and prepare the data set for use. Then to query and analyze the data we need to have a technical programmer to visualize, create a predictive model and present for use case analysis. Many companies are lagging behind as they find this cost centric in approach. However with proper reskilling initiatives there will be platforms and apps developed by teams that shall continue to make these tasks easier and more intuitive, and within 3 years we are going to get to a point where we feed the data straight into a single application that will handle all the remaining details with pin point precision.
- Intuitive Natural language processing
Locating relevant data as fast as possible could be facilitated through intuitive natural learning processing via a subset of AI. This dissects human language for machines to understand instantaneously. All people have to do is just ask questions in normal language and the system will answer back in ordinary language, with auto-generated charts and graphs wherever applicable. We saw this demo recently at IBM Developer DAY 2019.
- Database as a service (DBaaS)
Yes DBaaS and Big Data go hand in hand. We expect to see Database-as-a-Service (DBaaS) providers embrace big data analytics solutions over the next three years, as they have to adapt to serve a fast-growing client need. Enterprise companies have been collecting and storing more and more data, and continue to seek ways to most efficiently sift through that data and make it work for them. By integrating Big Data analytics solutions into their platforms, DBaaS providers will not just host and manage data, but also help enterprise clients to better harness it and enable developers to search and analyze data in real-time.
- Clean data
One of the biggest issues facing big data is clutter and incorrect data. Most companies have their own cleansing policies or develop policies organically. Few even get in touch with vendors like us for Data Consulting. Eventually the onus of cleansing and organizing will be automated with the help of various tools. As Big Data is not static these tools are expected to automate the cleansing process on a regular basis for quality and relevancy for quick data-retrieval to occur. Back in 2016, an estimated $3.1 trillion was lost in USA as a result of poor data quality. Hence ‘scrubbing’ through processed data or data appending is gaining relevance globally and real progress will be made for Big Data in the coming years.
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