Big Data is a set of large data containing varieties of data type and examining these Big Data is the process called Big Data Governance. This analysis is to uncover unknown correlations and pattern, customer preferences and market trends. For effective marketing, better customer services, new revenue opportunities, improved operational efficiency, and competitive advantages over rivals, the data analytics is the most essential part of an organization.
The basic aim of data analytics is to benefit companies to make decisions that are more informed in their business. Data scientists, analyse large volumes of business dealing data, also some of the valid big data components social media content, mobile-phone call details, survey response, internet clickstream data. Structured big data can be analysed by some of the advanced software tools such as data mining, text analytics, statistical analysis. However, the unstructured and semi-structure may not fit in the traditional analysis tools.
To some of the new organizations, big data is relatively new and significant for their business processes and outcome in this every day changing business world.
Following are some of the key practices, implementation of which could increase the chances of success.
1. Before gathering data focus on business requirements.
Before implementing big data, gather, understand and analyse business requirements; this should be the most essential step in the governance of big data process. For the specific business goals, planning big data is very essential.
2. Big data implementation is business.
Governing solutions for big data implementation is the most successful approach from business point of view, not from the engineering end. IT needs models while big data analytics are the solutions that perfectly define business needs.
3. Implementation approach should be active and repetitive.
Over the period, to implement specific use-case and data set, organizations must evolve data as they understand it after they had feel, touch and harnessed its potential value. Use agile and iterative approach for implementation to deliver fast solutions based on needs instead of using big application for development. From the practical point of view, big data governance is to start small with the specific, high-value opportunities while keeping eye on big picture.
4. Evaluate data requirements.
It is recommended to fully evaluate business data to know how it can be utilised best for the business’s advantage. Evaluate the input from your stakeholders. Together with analytics, analyse which data needs to be confined, managed and accessed, and what data needs to be discarded.
5. Optimize knowledge.
To share solutions, knowledge, planning views for subsequent use and ensure blunders for projects to minimize mistakes, organizations must establish Centre of Excellence (CoE). CoE also benefits in driving the big data and overall information structure in more architectural maturity and systematic way.
6. Embrace and align your scientist performance with cloud operating model.
Sometimes things may be difficult to know since technology is often touching new horizons and achieving new results. It is important to allow your data scientists to construct data experiments and prototypes in their preferred languages and environments. This will enhance their performance and after the success, the full proof concept is reconfigured with an “IT turn-over team.”
Resource management team need to control data flow from pre- processing to post- processing, integration, and in-database summarization to analytical modelling. In supporting change requirements, a well-planned strategy for public and private provision and security plays an integral part. In case of sensitive data where quick in-and-out prototyping is allowed, public cloud can be very effective, since it can be scaled up instantly.
7. Associate big data with enterprise data:
It is essential to associate big data with enterprise data. Enterprises open up new capabilities and influence their investments in platforms, infrastructures and business intelligence. These investments enable skilled workers to co-relate different types of data to make meaningful association and discoveries.
8. Using intelligence implant analytic and decision-making workflow routine.
For the competitive advantage in business, organizations need to form “the team of analytics”. Nowadays, for competitive success of data-driven organizations, they must have a team of intelligence that can embed analytic and decision making workflow. This team should be the part of day-to-day functioning.