One of the points I've been stressing for a long time now is that: It's not about data It's about business, the business outcomes, about the value that is generated for business. Business is the driver, data and what it produces is the enabler. As any other corporate asset, data's purpose is to generate business value. Organizations have apprehended the importance of data in their businesses and are looking deeper into data to gain a competitive advantage, implementing machine learning and artificial intelligence to achieve new business objectives and to move ahead of competitors in the industry. A data asset is every piece of data that organizations use to generate revenues, they are currently among its the most valuable assets, and organizations must invest seriously on managing these assets.

Most companies recognize the potential for data insights to improve customer experience, better direct marketing strategies, create new products and services, and optimize operations, among myriad compelling use cases. "If you need outsiders to tell you your data is valuable, you're living in the wrong century," says Wayne Sadin, an independent advisor and former CIO/CTO/CDO. Data is even more valuable during this pandemic period, when economies are volatile, markets are uncertain, and businesses face unprecedented challenges that underscore the need for intelligent insights to guide strategic decision-making. "The pandemic has already accelerated many organizations' digital transformation programs, and in many cases, data has emerged as an invaluable component of the successes of the modern-day enterprise," notes Sridhar Iyengar, managing director at Zoho. "Those businesses which are not already leaning on data insights risk being left behind." Defining and seeking clarity on how to value data as an asset.

As CEO of Hitachi Vantara, Gajen helps solve clients' problems by bringing to bear Hitachi's unrivaled industrial expertise across sectors. You might not think saving the world's tropical rainforests is a data challenge, but the urgent task of protecting the last remaining two million square miles of forest is precisely that. What is more, the challenge holds vital lessons for anyone tackling a data project with seemingly insurmountable odds. Logging, much of it illegal, strips the planet of more than 32 million acres of natural forest every year. If you ever imagined literally trying to find a needle in a haystack, then you might be able to contemplate what it is like to find a chainsaw in forested areas the size of Australia.

The effort to take advantage of emergent new business innovations, of advances in digitization, analytics, artificial intelligence, machine learning, internet of things or robotics, is leading to an increasing demand for people with related skills. Being a data scientist may be considered as the sexiest job within the data related jobs, but it has its challenges, specially when it comes to demonstrate the value created by their work. In this article, let us look at some of those challenges, and how they can be overcome when organizations take on a systematic approach on how to manage their data. This is often a communication problem, turning a business problem into a technical problem, when there is a gap in the language and concepts used by the business stakeholders and the data scientists. However, the causes run deeper, and can be related also with a lack of data literacy on the business side and business literacy on the data side, and with the lack of organization wide business concepts that can be clearly mapped into data.

In order to compete in the new digital economy, businesses must become increasingly data-driven. Few executives would dispute this objective. Recent events, including the global outbreak of COVID-19, have underscored the critical importance of having reliable data to inform organizational decision-making. Yet companies continue to struggle to operate in a data-driven manner. Even though we are now decades into the age of competing with data, a 2020 NewVantage Partners survey of C-suite executives representing more than 70 Fortune 1000 companies found that only 37.8% of companies have created a data-driven organization.

The application of artificial intelligence (AI) and machine learning to the business and IT, from intelligent IT operations (AIOps) to service management to software testing, is keeping the data revolution moving at lightning speed. That's why data science remains a popular concentration for computer science students who have the talent for math and analytics. And it's why more organizations are clamoring for data scientists who can help make decisions faster and put their businesses ahead of competitors. In today's age data science expertise with desirable knowledge in relatable fields is rare to find and therefore we have enlisted top 10 data science experts who you can follow in Twitter. Hilary is the Founder of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in Residence at Accel.

When the IT professionals of today went to school, they most likely studied things like programming. Students attending today's technical colleges and universities, however, are focusing on things like machine learning and artificial intelligence. As a result, tomorrow's workforce will have been taught an entirely new way of approaching IT problems, including cybersecurity. And since the problems and attacks IT teams will face are also evolving, this change couldn't have come at a more opportune time. Let's take a closer look at a few ways the IT leaders of the future will leverage AI technology to transform their organizations. Enterprise-level IT teams deal with complex processes and workflows on a daily basis.

In a 2013 report by IBM, the amount of data created everyday was estimated to be roughly 2,500,000TB. It very likely greatly exceeds this now, as wearables, AI, and connected devices have increasingly embedded themselves into society, gathering a veritable tidal wave of additional information for organisations to interrogate. This data comes in three forms: unstructured, semi-structured, and structured. Since the dawn of IT, structured data has been the main resource of analysts. Even today, this is the case.

What are you doing yourself? We work for the Chief Data Officer of a CAC40 company who even decided to make his 2017 tagline as "Zero POC!". Machine Learning and Artificial Intelligence are not anymore reserved to Amazon, Facebook or Google. However, very few initiatives (less than 1/3rd) deliver some measurable ROI. To overcome this hurdle, it is however somewhat rather simple. For a change, do not start only investing hundred thousands or millions euros building a data lake… We quite often see that a couple of hundreds megabytes already offer very rich insights to train predictive models. And plugging a simple but well designed bot with powerful NLP (Natural Language Processing) to your legacy BI datawarehouses free up tremendous hidden value.

The auction site says the acquisition will make it more "product-focused" by using big data to better connect shoppers to more than 900 million listings. Exact terms of the deal were not disclosed, but eBay confirmed that Expertmaker's staff will be joining its structured data product and technology team. Expertmaker has been an eBay partner for some years, helping the site to improve how users discover and purchase new products by working to optimise its database of listings from millions of sellers worldwide. "Expertmaker is excited to join the eBay team, bringing innovative artificial intelligence, machine learning and large-scale optimisation technology to eBay's structured data initiative," said Lars Hard, Founder and CEO of Expertmaker. "In partnership with eBay, we will be able to transcend traditional machine learning approaches to create adaptive and learning solutions using the Expertmaker platform."