How To Implement Big Data Analytics

How To Implement Big Data Analytics

Big data implies different things to different individuals. For me, it comes down to connecting frequently divergent and complicated data to important business levers to enable the proper decisions to address business issues. The most effective corporate executives are those aware of the levers at their disposal for enhancing performance. Real performance improvement may be achieved when big data is linked to those levers and used to improve decision-making.

Understanding the issues that need to be solved is the first step. What business levers do I have at my disposal to genuinely change how I operate and perform? For instance, a COO doesn’t merely look at operational budgets and makes indiscriminate cuts when faced with the need to eliminate tens of millions in operating expenditures. Big data linked to these cost drivers may significantly improve the results, especially when businesses are required to undergo significant operational changes. 

Following a planned yet flexible implementation path after deciding on your business levers will help you ensure that harnessing big data will provide you with the visibility you need to make the best decisions. 

How to Implement Big Data Analytics?

Here are a few simple steps of a data analytics implementation plan: 

1. Collect:  

The first stage is straightforward, but there is a signal: When gathering and synthesizing data, look beyond your local data sources and urgent needs. By definition, bigBig data is as complete as you can make it. The success or failure of the collecting stage will depend on the cross-functional understanding of features and capabilities. Enhance the perspective produced from your internal data sources by including external data. 

2. Validate: 

Raw data needs to be accurate and reliable. Too frequently, businesses will employ inaccurate data and mistakenly believe that analysis can cover up flaws. Strong project management is required to guarantee that data accuracy is adequate at this level. Do not simply invest in technology when dealing with big data; also invest in human capital. 

Process validation is also a good idea at this stage. Do you have the best scorecards and team to achieve your objectives?    

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3. Analyze:  

Before reviewing the facts, consider the final resultthe result. You will be able to get more from a good manager or consultant than “artificial intelligence,” which is just data that has been rearranged. While it may appear spectacular in chart or graph form, context is frequently missing. 

The ability to swiftly and simply repeat the analysis process is the ultimate test of data analysis. The analysis stage’s findings will not always be helpful or relevant if it is tough for you to compare different data sets or if getting the raw data is challenging.  

4. Gain Insights:  

Integrating several data sources to give the performance insights required to manage results is sometimes necessary. When commonality is found, it transforms into the “golden thread,” connecting vast volumes of data in a way that enables your management team to achieve objectives they weren’t able tocouldn’t before. 

Finding the golden thread in a sea of data takes systematic approach and intensive data analytics. To present a comprehensive picture necessitates a thorough grasp of the internal data sources and frequently calls for supplementation with external data.  

In Conclusion:  

Although big data is not a miracle solution, with the right implementation roadmap, big data analytics solutions may eventually give management access to your company’s real business levers, empowering them to make the change that directly affects your goals and objectives. 

Contact us at SG Analytics if you want to adequately implement big data analytics services in your business strategy 

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