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Big Data Challenges IT Professionals Face

As digitalization becomes a necessity in our daily lives, the information that organizations store to improve services expands.

As that data grows and transforms, it creates new dynamics between consumers and establishments. What’s turning into vital data? Well, some companies store the number of times you click on an ad. Others offer technology to your favorite coffee shops to ease your purchasing process.

Many businesses make sure the data they collect is managed effectively. But sometimes the processes don’t translate into practice quickly when challenges arise.

What is Big Data?

Big Data is a term used to describe extensive data sets. Information that comes from mobile networks, payment applications, web and social media are examples of Big Data that are generated in real-time and on an immense scale. In other words, Big Data can be collected fast and efficiently. These large amounts of information are often stored in computer databases and analyzed using software designed to handle complex data sets. The nature of Big Data reveals patterns and trends that have widespread applications, from fighting crime to psychotherapy to the supply chain.

Data scientists break down data into four dimensions known as the four v’s of Big Data:

  1. Volume –the size of the data sets
  2. Variety – identification of where data is coming from (e.g., unstructured data like emails and video files)
  3. Velocity –the speed with which data is generated (e.g., Twitter posts)
  4. Veracity –the quality of Big Data that is being analyzed. Data with high veracity has meaningful data and is processed using advanced tools like algorithms.

Analysis of Big Data allows organizations to make better decisions that they couldn’t have made using older data. Many industries are changing their operational strategies based solely on advanced data and analytics, according to a report by management consulting firm McKinsey.

Think of it this way: an online fashion retailer with advanced Big Data analytical tools can find out what a consumer’s broader needs are rather than focusing on random purchases. Big Data advances allow researchers to turn thousands of consumer data points into better predictive opportunities for sellers and buyers.

3 Big Data Challenges

Access to such a wide scale of information proves highly gratifying for different sectors of the economy. On the other hand, the more data an organization manages, the higher the responsibility is to make good use of it. In a recent worldwide survey, Gartner found that 90% of organizations haven’t reached a “transformational” level of maturity in data and analytics.

Most respondents said that adding value to data and solving risks are two of the most common challenges their organizations face. Another challenge within the management of Big Data is its human element. Who should work with the data and discover its potential? McKinsey found the following to be the top three Big Data challenges:

1.     Consumer Data Privacy

Handling billions of data records is becoming overwhelming for companies. So is keeping that information private. Big data sets can be attractive for hackers, especially Personal Identifiable Information (PII). As consumers become more sensitive to information privacy, companies must find ways to develop stronger security capabilities.

This includes their own approach to consumer information. In a special report for Forbes, Amandeep Khurana, CEO of Okera, made a call for companies to choose data privacy over profits. According to Khurana, big companies that abuse private customer data not only lose money but ruin their own brand reputation. While targeting consumers using some public data can be reasonable, companies need to make sure that their practices are compliant with federal laws.

2.     Adding Value to the Data

When it comes to data, it’s not enough to simply store it in case it can be utilized. Analytic research gains value when Big Data is used and applied to business problems that yield solutions. So, before launching a project or task, organizations should ask themselves what data sets are critical for their end goal or whether that data can create new sources of revenue.

For example, Amazon gathered enough data to determine that purchasing Whole Foods, a healthy grocery store chain, would increase their revenue. The large conglomerate took into consideration predictive behavior that showed consumer awareness of healthy eating habits.

Another successful adoption of data-driven strategies can be found amongst recruiters. Many of them use data-driven analysis to optimize their hiring process and increase the staffing of more productive workers.

3.     Hiring the Right Talent

The way data is collected, stored and analyzed can influence the likelihood of its value. While the demand for IT professionals is growing, organizations need to make sure that they are hiring qualified employees across all operational departments. According to the McKinsey report, business professionals can also learn to be proficient in analytics and influence change within companies. The key, the report says, is to “find people who can take the numbers, and then work them for the benefit of the business.”

Since almost every department in a company can use findings from Big Data analysis, leadership of analytics can become more collaborative. Professionals in healthcare management, for example, can be key players in administrative decisions if they have a complex understanding of hospital electronic health records (EHRs).

Recommendations for Managing Big Data

The McKinsey report lists recommendations for professionals that want to make the most out of big data:

  1. Following the data, wherever it is. A critical goal for a company of any size is to deep dive into data insights that can help it understand customers better.
  2. Organize valuable data. To avoid drowning in too much data, company executives should gradually build data sets based on priorities of information. Data and analytics strategies should be intertwined.
  3. Creation of data-discovery sessions. These sessions help organizations build data literacy and promote a data-driven culture.
  4. Allow the data to lead you. Rather than trusting a gut feeling, leaders should pay attention to statistical information and create digital infrastructures for performance indicators.
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