Pitfalls to Avoid When Building a Big Data Strategy
The massiveness of a big data is what makes it so difficult to handle. However, the advantages of it are so great that every organization wants to grab a piece of it in order to enrich their businesses. When used in conjunction with AI, big data helps in deciphering patterns and understanding trends. The resultant information you get through big data can be applied to future ventures as well as managing risks. Its undeniable importance is the reason why enterprises pay special attention to their big data strategies. In case your organization does not have the necessary skills to handle big data, you can train your employees by enrolling them in big data courses online.
Fundamental knowledge of the Microsoft Windows platform is essential for anyone who wants to learn about big data. Most system administrators are accomplished in Windows platform and they can be taught about big data. Either you can train your employees and make them familiar with the concept of big data or you can hire from outside. In the US, you can hire a Data Scientist for an annual package of $97,642 and a senior Director of Analytics can be recruited for $135,764 per annum.
Avoid These Pitfalls When Building A Big Data Strategy
There are certain mistakes that companies make while devising big data strategy, which should be avoided for reaping maximum benefits from it. Below is a list of major pitfalls:
1. Underestimating The Quality Of Data
As an organization, you should pay special attention to the quality of data that you are using. The quality of big data can decrease considerably when you integrate unstructured and semi structured data into the data sets. Any problem with the data should be resolved proactively before adding it to the data pool. You can use language correction libraries prior to processing big data, so that it improves the quality of the data pool. In case language translation is involved, a database manager should provide appropriate contextualization for each linguistic connotation. For semi-structured data a large amount of end-user inputs are needed for ensuring the validity of the data and its context.
2. Overlooking Data Granularity
Big data is ambiguous by nature, which means there is no clear definition for the grain of data present within the data. The granularity is only discovered when the data is processed and forms a data set. For instances, where data sets are unable to identify the right amount of granularity the chances of erroneous data increase, which can hamper the analytic outputs. An organization should always look for granularity of data before using it as part of big data.
3. Not Paying Attention To The Governance Of Data
One of the biggest challenges of governing big data is the inherent complexity of processing big data. The business rules definition that is laid for processing the data and the multi-owner–based stewardship makes the complex task even more difficult to govern. An organization has to learn that big data needs sponsorship and guidance from executive in order to gain acceptance within a company. Without proper governance, any program will fail and hence, it is essential to pay special attention to the governance of data.
4. Not Using Vision For The “Data Lake”
With Data Lake you get to capture “as-is” or “raw data” before any data transformation or schema creation. Data Lake provides a unification of different types of data. A Data Lake repository can provide powerful major benefits via the “economics of Big Data”. By using Data Lake, you can get up to 30x to 50x lower costs when compared to traditional setups.
5. Relying On Algorithms Instead Of Big Data
Some people believe that advanced algorithms will be able to produce better results even with a limited quantity of data, which is not true. In dearth of large amount of data, even the highly sophisticated algorithms can present nonsensical results. Never discount the importance of the quantity of big data while processing it with advanced algorithms.
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