Data Value Chain

Big data is generating a big buzz in today’s business world. With all the recent hype surrounding big data, it seems like only a matter of time before everyone will be on board to join the bandwagon. For most companies, big data is no longer just a technology initiative; it’s a business program, which requires specific technical know-how and process expertise.

To get in the big data game, companies needs to focus on three core steps:

  • Data itself - Companies needs to gather large quantities of information in such a format that allows easy access for analysis.
  • Tools and Platforms – Companies need to leverage the advanced analytical tools, such as NoSQL or Hadoop.
  • Expertise – People should be skilled in everything and not just in interpreting data or understanding privacy laws. In other words, people handling data should also have an understanding of the business and the relevant sources of value.

With these basic guidelines in place, companies should work on creating a data value chain. Hence, gathering data should not be the only agenda, but there must be a system in place that ensures that the quality of that data is correct, relevant and useful. With a data value chain in place, companies will be able to get best insights from big data.

Here are a few keys steps to create a data value chain:

  • Identify the Goals and Objectives: Companies need to identify objectives for their data team which will then require them to work on collecting significant data and analyzing that data based on the identified goals.
  • Data Collection: The bigger the data, the better it is - companies will need to cast a wide net for data from diverse sources. As more data not only helps in finding better correlations, but also is better put to use for more actionable insights.
  • Data Cleaning: Data quality is a key component of data analysis. The buzz associated with big data lies in its analysis. And this “analysis” depends critically on data quality; which makes up a large chunk of the whole data science. Hence, companies need to have a team of data experts or scientists to correct spelling errors, fill in the missing data, or weed out all irrelevant information. Junk data will always be a hindrance for better analysis, making this a very critical step in the data value chain. Essentially it is very important to take continuous steps to validate and clean the data for new insights and actions.
  • Data Science and Data Modeling: This is where a team of data scientists come in to derive meaningful correlations based on the data (collected and cleaned) to predict new opportunities, discern patterns and make better decisions to scale the business. Hence, it is a good bet for companies to have an all rounded data science team which can be a mix of - people with a degree in statistics, engineers, software developers, ETL and data entry experts to build the necessary data collection infrastructure, data pipeline and data products.
  • Optimize this chain and repeat the process: Companies must continue to improvise on this data value chain based on what works and what do not for their business. Business teams and data science teams should work hand in hand; business teams taking actions based on the data reports, whereas data science team should continue to improve data collection and data cleanup for better analysis reports. A good data value chain is indeed a critical process for the business to scale and accomplish its set goals based on accurate predictions and analysis and move on to the next business challenge.