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Soaring too close to the sun’ might have reminded you of Icarus from Greek mythology. Icarus was the son of Daedalus, a skilled craftsman who had created a labyrinth for King Minos of Crete Island. Best Cloud Server.

The king, however, did not allow Daedalus to leave the island. Daedalus and Icarus were held prisoners inside the labyrinth. Adamant to escape, Daedalus built two pairs of wings out of wax and fled from the island. As they began flying, Daedalus warned his son of not flying too close to the sun.

Icarus neglected his father’s advice and was tempted to fly higher. He soared so high that the heat of the sun melted his waxwings. Eventually, he fell into the ocean and drowned.

In western culture, Icarus’s actions symbolize folly and excessive ambition. His lack of foresight can be compared to our greed for more and more data, even when the data may not make any sense.

With new strides in technology, we are moving towards an age of data deluge. We are generating a gargantuan amount of data every minute. Online transactions, devices, sensors, social media, business applications, and search engines all are contributing towards this deluge.

Given the rate at which we are generating data, we may end up producing 463 exabytes of data per day by 2025! Best Cloud Server

We believe the more data we gather, the better it is. We feel complacent in being able to accumulate mounds of data but our rate of data generation and collection does not match with our ability to leverage the same to our advantage.

You can compare this to a situation when a city experiences massive population growth. Initially, the population appears to be an asset, but beyond a point, the city infrastructure is unable to fully exploit the growth and starts crumbling.

We need to remember that data and information are two different things. Because we base our decisions on information and not data, understanding the distinction is vital.

Then extracting suitable information from the data has its own set of challenges. You may not be able to appreciate the difference between correlation and causation. Or, your data may not be updated to reflect the latest findings. Or, you may not know how to prune outliers and bad data points or fill in the missing values.  As a result, you may end up with hordes of graphs, charts, and statistics that provide misleading information.

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Most organizations lack vision when it comes to data collection and analysis. They collect data merely because they can. And try to analyze every bit of data they have at their disposal. This approach leads to chaos and confusion. If decision-makers don’t deliberate on why they are analyzing data, they may find themselves lost and not find appropriate answers.

Data deluge also creates the problem of dark data. Dark data is the unstructured data organizations collect and process during regular business activities but fail to use for other purposes. 90% of the unstructured data falls in this category. Customer call logs, employee information, email correspondence, and IoT device logs are all instances of dark data. If not handled properly, this growing pile of data can create an array of issues.

Organizations can lose sensitive or proprietary data on products, financial plans or business operations leading to an impact on their business. If they somehow misplace the data they collect from the clients, it can cause a loss of reputation. Inability to handle large amounts of data can throw organizations into legal and financial liabilities.

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So, what is the way out? How can you put this burgeoning data to strategic use without letting it transform into a liability?

1) Begin with your business objectives. Before you start collating or analyzing data, try to answer one question-Which business problems are you trying to address through data collection? Which long-term or short-term business objectives are you trying to fulfill through your analyses?

2) Identify the steps that need to be taken to fulfill the objectives mentioned above. Because you need to concentrate your data collection efforts, you should focus only on measures that seem to provide answers you are seeking.

3) Map out all the sources that are generating data, the locations, online/offline where this data is being stored and personnel who have access to the data. Keeping a track of data generation and usage paints a clear picture of data availability and minimizes data loss.

4) Go beyond the information provided by data analysis. While the dashboards and statistical figures do offer valuable insights, it is important to conduct a comprehensive analysis to find out the root cause of a problem.

5) Prioritize which problem to solve first. Your business might have several problems to tackle. Start with the most urgent.

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