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The Pathway to Success in Big Data Featured

The Pathway to Success in Big Data Lili Popper

Big data has, over the years, gotten bigger, better and more useful, and so have the expectations of people and organizations on its potential. With the continued adoption of this technology and digitization, big data will continue growing. Regardless of your database or application, it would be best if you had the flexibility to connect quickly and consolidate the data. With the advances in big data, you will also need a strategy for a pathway to success in your endeavours. Here are some best practices for approaching your big data and realizing its full potential.

  1. Think long term

Many people are worried about staying current with big data technology. With everything evolving so fast, it is impossible to identify the tools, platforms and approaches that will be best for this year and the coming one. Despite the changes, you need to relax and stay open to the possibilities of new products as long as they deliver enough value for justification of availing them in the current environment. With the rapid technological changes, some of the most crucial components you require for operations, such as Hadoop and data warehouses, will keep evolving quickly. Maintain a business intelligence platform with the potential to connect you to different formats.

  1. Blend the right big data at the right time

Most big data companies have big data readily available. However, they fail to use it effectively. Turning big data into success always comes with challenges. Turning big data into success presents various challenges, and therefore organizations need to set their priorities right for extracting actionable insights. In a majority of big data companies, data is available. However, the problem is that it is not complete, organized, stored and fused in the right way to allow direct consumption or use to make decisions. Using big data analytics effectively will help firms address novel problems by identifying bottlenecks in business plans and finding the inefficiencies in processes. 

  1. Define a definite company structure

Companies with dedicated predictive analytics business units have a better success rate than those with ad-hoc or decentralized teams. Companies can use big data analytics with a centralized setup for the analytics team. Centralization will help companies bring business leaders together and big data technology to address and develop use cases and outline best practices that allow teams to leverage it.

  1. See through false choices

As an organization, it isn't easy to find everything you need in your organization. Whether it is Hadoop or a data warehouse, you must identify the best choices for your case or a combination that can benefit you. The data warehouse is the best choice for understanding crucial structured data and storing it where BI data tools and dashboards can easily locate the stored data. However, the challenge with this option is that it is slow for analytics processing and can be challenging for some types of transformation. Hadoop can help address that. Hadoop is weak in interactive queries and data management but good at taking in raw, unstructured and complex data. Data warehouse and Hadoop can form a symbiotic relationship.

  1. Find a strong leader to drive big data initiatives

Leadership is everything when it comes to attaining success in big data initiatives. Organizations must have well-defined leadership roles for big data and analytics to be successful and implement big data initiatives. With the right leadership, an organization will have proper stewardship that will improve the company's daily operations. According to research, only 34% of companies have appointed the strong right leaders, including some like Chief Data Officers or equivalent, to ensure successful implementation of big data initiatives, which is a key challenge to their projects.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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