Data within business environments has grown exponentially over the past decade. By 2020, it was estimated an average of 1.7 megabytes of data would be created by every human being on the planet every second. This equates to around 44 trillion gigabytes of information and a projected 50 billion connected smart devices used to move, process and store this information.
With the sheer volume of raw data organizations are generating and need assessed, data analytics has become a non-negotiable part of the 21st century business. Data analytics is the process of analyzing raw data in order to create meaningful business decisions. The data analytics process involves a number of automated processes and the use of algorithms to derive information companies can use to forge more efficient and effective business plans.
Though the objectives of organizations conducting data analytics may differ, the process often shares similarities;
- The initial step is to understand how the information must be grouped. The demographic factors that are to be highlighted are understood and the data values are categorized for ease of comprehension.
- The second step is to collect the raw information from multiple sources. Here the organization must ensure the credibility of their sources to make sure the information obtained from the refinement process is valid.
- The third step involves organizing all the information obtained into multiple buckets to be analyzed. This is often done using a database or other software.
- Finally the data must be scrubbed clean for errors, duplication and incomplete data before the analysis process can begin.
Once the analysis begins, there are four key types of data analytics derived from the raw data;
- A descriptive analysis that recounts information over given time frames including whether sales have improved or whether engagement has gone up
- A diagnostic analysis that helps reveal causation of certain events. This includes impacts marketing campaigns have on sales. This type of analysis uses a number of data sources and can include some hypothesizing
- A predictive analysis explains what is likely to happen in the near future. This is exceptionally healthy when understanding risks is needed and using past data to help create a clearer picture of the present and possibly the future.
- A prescriptive analysis acts as a remedial measure in case something goes wrong. This includes crafting comprehensive contingency plans.
There are a number of benefits to running data analytics services within organizations in any industry and at any size. Companies are harnessing the power of data analytics in order to;
- Identify areas of potential savings
As introducing data analytics solutions can often be cost intensive, companies must understand the long term benefits of the process including the discovery of potential areas where long term running costs can be lowered.
- Identify the potential for quick wins
Introducing a data powered decision making process allows businesses to understand their market and surrounding markets simultaneously. This allows businesses to understand areas for potential expansion that can be tapped into quickly, and with a direct plan of attack. This means a shorter business cycle and quicker revenue generation.
- Identify potential partnerships
Data analytics offers information around competitor companies and complementary organizations. Businesses can use the results of this process to develop partnerships or tie ups with corporates that could augment their reputation. This allows the company to tap into a new market with the added benefit of insider information and some hand holding.
- Introduce optimized decision making
Data analytics helps companies understand the best course of action using logic and statistics. Companies can use this information to streamline internal processes to move through business cycles faster while creating higher value output and minimizing waste.
Other benefits include a ground level understanding of customer issues, being able to understand where the company stands in the market, maximizing human capital and introducing better strategies for data management. Organizations new to this space are likely to look up established data analytics companies that offer data analytics services based on unique specifications, needs and budgets. Data analytics is an integral part of staying competitive within modern markets and should not be overlooked.