7 Big Data goals in 2021

Big Data leaders from the corporate sector have high hopes for this year. Needless to say, 2020 was not a normal year by any means but still corporate organizations have planned well ahead for the future. It seems that the corporates are focusing majorly on the improvement of data quality and turnaround of big data projects.

  1. Better Data Management – Corporate networks are witnessing the influx of big data at a higher rate than ever which is the reason why focused efforts needs to be put in so that data is screened as soon as it comes in and also to sufficiently clean and prepare the data before the process of adding it to corporate data repositories is completed. The data is also needed to be cleaned and prepared after it passes the incoming criteria before it can be uploaded into a data repository. This process is essential because it checks for incomplete, duplicate, and inaccurate data and what this process also does is normalize data so that no issue is faced while blending it with other source data for the purpose of analytics.

Example – IBM Research, Switzerland stands witness to the fact that how with efficient data management a lot can be achieved in a short time span. At IBM Research in Switzerland  researchers were assisted with AI and Machine Learning in going through a large number of scientific papers and journals which was required to find out information that carried some relevance with regards to a molecular drug design. Researchers didn’t have to waste much time before realizing that major chunk of the global data that the AI would be looking for have no relevance to the issues they wanted to address. The outcome was quite impressive as well, the company took a swift decision to get rid of importing data from sources that are absolutely irrelevant, as a result of which hours of AI time was saved, researchers were able to work with a set of data that is highly relevant and it also helped with the issue of data storage waste.

  1. Process Monitoring and speed – 2021 is being looked as the time where it is imperative to settle down with the iterative DevOps style development approach for Big Data and analytics so that it is possible for data analysts to be aware when a big data analytics model is prepared and fit enough to be placed and maintained in production.  A big data analytics results must return an accuracy rate of 95% consistently to be considered ready for corporate organizations. Keeping ever changing external and internal conditions in mind, it can so happen that a big data application isn’t being 95% accurate. It is imperative to have a maintenance policy in place handled by IT and Data science to ensure that the applications are still returning extremely accurate results.
  2. Hybrid Architecture – There is no doubt about the fact that 2021 is the year where Hybrid Architecture gets formalized. Some big organizations have already done so and now it is time for other organizations to follow suit. There is an ever growing need for data to be pulled from very different sources and for that very reason it is necessary to have an over-arching hybrid cloud architecture in place.
  3. Bridging gaps between IT, Data Science and users –  Simplification of AI solutions by different vendors has resulted directly in the growth of citizen AI which lets business units come up with their own AI and big data applications. When users want these applications to be integrated with their systems that is when the active assistance is required of IT and Data science departments. In 2021, it is absolutely necessary to ensure that IT and data professionals  cooperate with users early on in the development process of their business applications to ensure that difficult issues with integration can be avoided.
  4.  Improvement of security especially on IoT devices – There is no other option but to make sure that security standards are much better in 2021. It is well known that due to OS of IoT devices they are prone to security issues. It needs to ensured that these devices conform to the basic standards of corporate security, which can be achieved by working together with vendors and reviewing these devices.
  5.  Dashboards –  The dashboards which come up as a result of analytics might seldom have issues from a technical perspective but it is necessary to make sure that there is still some sort of relevance attached to the results. In 2021, it is advisable for the IT department to review these criteria annually with end users and make necessary changes.
  6.  Reducing communication gap with management –  In 2021, it should be a big goal to  make the management actively participate on projects and new developments. In spite of the management being aware of the crucial significance of big data, it still needs to be ensured that management is properly communicated about regular projects.