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“He Who Rules the Data Rules the World”

It is no doubt that the world’s most valuable resource today is not oil but data. Welcome aboard industry 4.0, also known as the fourth industrial revolution or quite commonly “The Triple D’ Revolution referring to Data, Decentralization, and Digitization of the world. Data is important not only to businesses but in all sectors. It helps to identify problems and opportunities, develop solutions, and look into the future. However, data only becomes useful after valuable information has been extracted from it.

What is Data?

Any set of statistical values, qualitative or quantitative based on verifiable facts that can be used as reliable information to make sound decisions can be classified as data. After the 2008-2009 market crash and financial meltdown, the global trade and economic space underwent a timely evolution, anchoring towards the three D’s (Data, Decentralization, and Digitization). This was the genesis of the ‘Data is Power’ dispensation.

In a recent study, 81% of 400-plus senior executives from industries across the globe have had “significant” or “very significant” success with their business intelligence programs which are purely data-based.

In another study, two-thirds of businesses have undergone, or are preparing to undergo, a full digital transformation, enabling them to capture every bit of data on customer commerce and communication to improve operations and enhance customer experience.

These findings have prompted many experts and analysts to hail data as ‘the new oil’ or ‘the next gold’. However, there’s a difference in value between data and the said ‘gold’ or ‘oil’. While oil and gold derive their value from scarcity, data, on the contrary, derives its value from abundance. So when it comes to oil and gold, less seems to mean more in terms of value. But when it comes to data, more actually means more!

Consequently, executives now have a new headache in managing and processing data efficiently through effective strategies. When running predictive analytics, for instance, projections must be based on complete and correct data, failure to which projections will provide limited value potentially creating negative values by falsely amplifying results across all data sets. Without proper data processing, therefore, companies limit their access to the very data that can give them a competitive edge and deliver critical business insights.

What is Data Processing?

Data processing, a role delegated to data scientists, refers to a series of procedures involving the collection of data in its raw form and translation into a final format (graphs, documents, etc.) that is user-friendly and easily interpretable. This involves data being manipulated into a language and context necessary to be computed by machines. These machines then transform the data into meaningful information that can be utilized by employees throughout an organization to solve a problem, improve a situation, or make a decision.

Similar to a production process, data processing follows a cycle where inputs (raw data) are fed into a processor (computer systems, software, etc.) to produce output (information and insights). This cycle includes activities like data entry, summary, calculation, storage, etc.

Six stages of data processing

Here are the six stages that data passes through during processing.

1. Data collection

Collecting or gathering data is the first step in the data processing cycle and arguably the most crucial since the quality and quantity of data collected have a direct impact on output. Data is extracted from various sources, including data lakes and data warehouses. In addition to data being high quality, it is important that it is obtained from credible sources.

The collection methodologies applied ought to make it possible to gather data that is both well-defined and accurate so that the subsequent decisions based on the findings are valid. This stage provides both the baseline from which to measure the output from data as well as identify what needs to be improved.

2. Data preparation

In data science, the famous saying ‘garbage in, garbage out’ holds very true, thus raw data cannot be processed without preparation. This stage, often referred to as the “pre-processing” stage, is where raw data is screened for errors and inaccuracies. It is then rid of all junk and validated as having integrity. Afterward, it is profiled and constructed into an organized data set where data from one or more sources are weaved into a form that is suitable for further analysis and processes like programming.

3. Data input

The screened and validated data is coded or converted into machine-readable language. Data entry is done using a keyboard, scanner, or data entry from an existing source. This time-consuming step requires speed and accuracy since most data need to follow a formal and strict syntax. Since a great deal of processing power is required to break down the complex data at this stage, many businesses are resorting to outsourcing this process to cut costs.

4. Data processing

The processing stage is where input data is converted into information for interpretation purposes. It involves the flow of data through a series of predefined computing procedures that rely on complex machine learning and artificial intelligence algorithms or other processes. This process may vary slightly depending on the data being processed e.g.

data lakes, social networks, connected devices e.t.c. and its intended application, for instance, forecasting sales or determining customer needs. Such applications as Apache Storm, MongoDB, and Cassandra are available for processing large volumes of heterogeneous data within very short periods.

5. Data output/interpretation

This is the stage where non-data scientists, analysts, and other business stakeholders come in. Data has finally been processed into usable information allowing them to interpret and present it in easy-to-understand report formats like videos, graphs, images, and others. Various departmental members can now make use of the reports for various purposes to gain diverse insights that will inform their decisions, objectives, action plans, and strategies.

6. Data storage

The final step involves storing processed data for utilization in the future. Proper storage is not only a compliance requirement by GDPR (General Data Protection Regulation) but also crucial for easy access by employees of an institution and other stakeholders as and when needed.

Conclusion

As data experts would say, data is only as valuable as its integrity and its ability to be accessible to those who need it when they need it in the form that they need it. This is what the entire data processing cycle is designed to achieve. In a nutshell, data collection gives us the ‘what’ in the right volumes; data preparation gives it integrity and quality; data input makes it machine-readable; data processing makes it human-readable; data output and interpretation makes it presentable and user-friendly, and finally, data storage makes it accessible and durable.