Businesses of all shapes and sizes are pursuing the data-driven transformation process, driven by the need to generate information to generate new revenue opportunities, increase efficiency, and deliver business-to-business solutions. innovative products and services.

In a survey conducted by MIT Technology Review Insights of 2,300 global business and IT leaders, in combination with Pure Storage®, 87% of respondents said data was critical to new business growth and improved results for customers. Eighty-six percent of respondents considered data as the basis for making important business decisions.

However, simply collecting and managing data does not guarantee results. Companies face many challenges in transforming data into meaningful business value. Sometimes the hardest part is knowing where to start exactly. "Companies need to determine what is worth the initial investment in time and costs for immediate return on investment. Does it even have a realistic expectation in their industry? "Says Dominic Halpin (@domhalps ), founder of TechNative.

Data Management 101

From there, it's all about understanding the company's data landscape, including what's available and how to effectively map it to the bottom line. "The biggest challenge is knowing where the strategic data is, understanding the data definitions, and developing reusable analytics that answer a variety of business questions," says Sacolick.@nyike), President of StarCIO, author of Digital drivingand editor collaborator at CIO and InfoWorld. "The three challenges are magnified by the increasing volume, speed and variety of data, as well as the need to implement data governance."

Organizations should start by asking the right questions and determining what data is needed to develop this information. "At one end of the process, you ask questions about the data. On the other end, you want results that lead to actionable information, "said David Geer (@geercom), cybersecurity journalist.

The smooth running of this process has everything to do with your success. "If you ask the wrong question, you risk a slowdown by focusing on something that is not necessarily relevant," says Jason Wankovsky (, chief technology officer and vice president of services. Mindsight.

Framing the data with context is another important part of the mix. "Unfortunately, most databases do not include this context," says Kevin L. Jackson (@ Kevin_Jackson), founder of the GovCloud network. "This is a re-enactment of this context and its coupling with current data repositories and past the # 1 challenge for today's decision-makers," he says.

One of the most important aspects of any kind of data initiative is data quality and data governance. The lack of consistent strategies, the multiplication of similar data copies, the aging and duplication issues, and the lack of integration between complementary data types make it difficult to use advanced analytics, notes Larry Larmeu.@ LarryLarmeu), leader in cloud technology at Accenture.

"How do you bring all of these together in a meaningful and efficient way, making sure your data is stored in a unique and quality location," said Tim Richardson (@OCSL_UK), Enterprise Architect for OCSL, belonging to the CANCOM family. "The saying" garbage in – garbage out "applies here.

The timing is also critical. If information is not provided to the right people at the right time, it loses all impact. "If the information is not quickly transmitted to the relevant stakeholders, it may be ignored or become too outdated to be used," says Phil Siarri.@philsiarri), founder of Nuadox.

The same can be said of the creation of a data culture. "Comprehension tools tend to be developed in-house and work in silos – even a" ready-to-use "solution would be an improvement," says Hugo Harris. (@ Hugo_Harris), co-founder of Kraytix. "As such, they need data specialists to tap the information, and even then they are not exploitable"

Welcome to AI and machine learning

Machine Learning and Artificial Intelligence (AI) capabilities, both of which are important for finding the proverbial "needle in a haystack" influence, are being incorporated into new analytical tools and new data management platforms. However, machine learning and artificial intelligence are new skills for most computer companies, who are sorely lacking in expertise in the field of data science and even less of a problem. Bench of skilled talent.

"The problem is that data is not intelligence: people do smart things, find valid models to identify signals, and contextualize context that turns context into perspectives, the challenge remains. to take up, "says Wayne Anderson (@DigitalSecArch), Security Architect at McAfee.

This talent can be difficult to find given the limited universe of qualified experts and the fact that demand for their services is booming. "Companies and public sector organizations recognize the unprecedented shortage of data scientists," says Will Kelly (@willkelly), technical writer and responsible for content development. "Although I'm expecting to see more strategic and more comfortable companies with technology set up internal training for business users who need to use low / no code tools to pull valuable data, many organizations are not yet at this point. "

Algorithms, the mathematical models underlying computation and automated reasoning, can also pose problems of precision. "For a given application, the first attempt to learn can produce an algorithm with an accuracy of 60%, which is considered mediocre," says Brent Kirkpatrick.@ BrentKirkpatri3), founder of Intrepid Net Computing. "It can take years to customize the algorithms to produce 90% accuracy."

Data security is great

Since data-based information and analysis are the hallmarks of the modern organization, it goes without saying that security is a top priority.

The principles around Zero Trust, which assumes that the network identity, access points and traffic can potentially be compromised, must therefore be checked regularly, should also be applied to inactive data, says Valentin Bercovici (@ valb00), founder and CEO of PencilDATA. "With the outbreak of malware and data breaches, the integrity of my data is now subject to a reasonable doubt," he said. "My challenge # 1, extracting valuable business information from data, determines the veracity of my data."

It is no longer sufficient to have a system or layer of security and management information (SIEM) in commercial threat data, to deploy a deception system or to prioritize assets – there is no need to simply no single security solution. "It's more art than science," says Kayne McGladrey (@kaynemcgladrey), Director of Security and Information Technology. "An effective solution must incorporate elements of all these products or solutions to create meaningful and actionable intelligence."

As companies make great strides in addressing these and other challenges, the search for data-driven solutions and business transformation based on analysis continues to be long and winding.

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