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We hear a lot nowadays about “data literacy,” however not many stop to consider what the term truly implies. When data is seen as the backbone of companies, it’s important that organizations must help their employees to utilize data appropriately. All things considered, spending on big data and analytics products are relied upon to obscure $200 billion by 2020, up from $150 billion a year ago, as per IDC. To take full advantage of these investments, organizations must teach their employees to utilize data appropriately.

The study, which Qlik appointed for the Data Literacy Project, reported that most organizations today favour job candidates who have proven data skills ahead of those with higher educations, including data science degrees. The study found that only 21% of U.S. employers saw a degree as its essential consideration while employing for any position, compared with 64% who are searching for candidates who can show their data skills.

What is Data Literacy

Data literacy is the ability to read, work with, analyze and communicate with data. It’s a skill that engages all levels of workers to pose the correct inquiries of data and machines, build knowledge, make decisions, and convey significance to others.

Okay, so it doesn’t simply involve understanding data. To be educated, you likewise need to have enough certainty to question data that isn’t acting in the manner that it should. Furthermore, sure, it is highly unlikely to know it all. Also, to distinguish abnormalities in vast amounts of data is marginal impossible. Or maybe, literacy helps the analysis process, taking into consideration the human component of critique to be included.

Organizations are looking for data literacy over all occupations, not only for data and analytics positions, the study found. The overview, which was authorized by Qlik’s Data Literacy project, firmly recommends that companies that aggressively invest in data literacy programs will beat those that don’t. As indicated by Qlik’s Data Literacy Index, companies with higher rates of data literacy have 3% to 5% higher values, which interprets $320 million to $534 million higher valuations for each organization.

Why Data Literacy

Most organizations have at least a handful of data analysts who mine bits of knowledge for the organization, however, data literacy happens when the majority of employees have information readily available.

Accomplishing data literacy has several components. Tools and technology are a part of it, yet employees should likewise figure out how to think about data, so they can comprehend when it’s valuable and when it’s most certainly not. What’s more, maybe, in particular, data literacy requires a culture wherein data is valued by all as an essential vehicle for decision making.

Morrow has characterized seven key elements of a data culture. Above all else, data literacy ought to be encouraged to spread. Individuals who don’t have the foggiest idea of how to discuss data, the words and the language, are off guard with regards to helping the data culture spread.

“It resembles heading off to a remote land and not completely being prepared where they don’t communicate in your language,” he says. “They’re going to hit detours everywhere. I call communicating in the language of data, or data fluency, the secret sauce of data and analytics strategy and data literacy success.”

When employees approach data, they should have the option to see it, control it, and offer outcomes with team members. Numerous individuals default to Excel on the grounds that it’s a familiar tool, however, keeping data to a desktop application is constraining and prompts irregularities. Information gets out of date and employees get clashing outcomes even when they’re evidently taking a look at similar figures.

Having a common platform for viewing, analyzing, and sharing data is useful. It gives a single source of truth, guaranteeing everybody approaches the most recent data. It’s additionally a lot simpler to enforce policies around security and governance when data is centrally stored and managed.

Having strong analytical, statistical and data visualisation skills are other key elements of data culture. With data visualization, complex data can be made simple and simple humans drill through data to find solutions to questions. If you can communicate in the language of data and have data familiarity, a person who doesn’t know how to do the statistics can at present speak with the individual who does.

The willingness to learn is another key element of a data culture, Morrow says. We should be eager to commit errors and move on from them. The other element is coaching others to spread knowledge and insight. To wrap things up is data storytelling, which can catch the creative mind and make complex data more accessible.

The business intelligence industry has been prospering for quite a while. However, a later improvement is automation. In spite of the fact that procedures associated with business intelligence and data analysis have consistently been fairly computer-driven, this has expanded and keeps on increasing every day. With this, another question has started to surface-What will be the role of data analysts in the future?

This is the place data literacy comes in. Numerous experts accept that the human component will even now be fundamental within the analysis process, paying little mind to the level of automation that we reach. So maybe this is the reason individuals are ramping up their efforts. We can see a brief look at what the future of business intelligence will be, and to fit in, we all need to be data literate.




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