Data by itself is not useful unless

Data by itself is useless. Data is only useful if you apply it. To think about it, Todd Park has explained it very rightly. Businesses today are churning out humongous amounts of data minute after minute, day by day. While data is being generated at a pacing rate, not much of it is put to the right use. With the rising need to be data-driven, businesses are looking at the evolution of data to bank upon. So how exactly does data enable businesses to take a leap forward? Raw data is nothing but data which cannot be turned into information. Considering the data being thrown out by the systems today, one can easily point out the isolated data silos in an organization. To get the best out one’s collected data, the dots in the puzzle (data silos) need to be connected or as we say in technology terms, to be integrated. With this integration, businesses can reap new data patterns, analyze the insights, and understand the trends in their data in a single point of data access. The right technology aid and automated enterprise solutions thus enable businesses to get more out of their unused data. The evolution of data requires technology to walk hand in hand to drive businesses ahead. Unlock the true power of your data with the right technology partner and transform your business to be Data Smart. What are your views on the evolution of data? Do you see a rising trend? Share your views in the comments section below.

As the data economy heats up, the fear of being left behind leaves some company execs in a cold sweat. Forward-looking board members ask about data commercialization strategies and expect answers. That question – “what’s my data worth?” – is top of mind for most. Many business leaders have struggled with this question for years. The answer has been elusive in part because people have been looking in the wrong place. There is no inherent value in data. Data is without a doubt valuable. But when stored in vaults and locked down, it is not. As the Americaninventor, Thomas Edison asserted, “The value of an idea lies in the using of it.” That sentiment was rephrased in modern terms when Christina Ho, U.S. Deputy Secretary of Commerce said, “Data + use = value.” It isn’t until you know how the data will be used that you can determine its value. And, it’s like a brick or a piece of wood or the first fax machine: linking it with other data adds even more value. Insights-driven companies derive value from their data. They systematically use their data to deliver better customer experiences, improve operations and create competitive differentiation – all of which add to the bottom line. While most companies don’t attribute those results to the data itself, agrowing body of researchillustrates the interest in and the difficulty of estimating that “enterprise value of data (EvD).” Many valuation methodologies focus on the goodwill entry on a balance sheet, legal settlements over priva...

E-commerce platforms have brought a veritable Aladdin’s cave of products right into our homes. As a consumer, you no longer need to go to a shop, instead, the shop will come to you. Digital platforms such as these amass a vast amount of data from their customers, which they analyse to enhance the customer experience. Once customers are happy with an online shop, they begin to trust the seller and will likely frequent the shop again. The world’s largest online marketplaces, for example, Amazon or Alibaba, have revolutionised competition and the rules of traditional trade. Information management and artificial intelligence make shopping more and more addictive, but an astute customer can also reap the benefits of online shopping. From multichannel to omnichannel Back in the 1990s, shops managed each of their sales channels separately. The brick-and-mortar shop operated as one entity, with an online shop as another separate entity and telesales as a third. At that time, shops obtained consumer information mainly from receipt data. With the advent of loyalty cards, more detailed customer information began to be gathered. ‘This multichannel approach was followed in the 2010s by an omnichannel one, where the customer decides where they do business. From the consumer's point of view, the shop forms one entity, in which they may first visit a bricks-and-mortar shop to physically see the product, which they then order online. Transactions are independent of time and place,’ explain...

It is clear that a new age is upon us. Evidence-based decision-making (aka Big Data) is not just the latest fad, it’s the future of how we are going to guide and grow business. But let’s be very clear: There is a huge distinction to be made between “evidence” and “data.” The former is the end game for understanding where your business has been and where it needs to go. The latter is the instrument that lets us get to that end game. Data itself isn’t the solution. It’s just part of the path to that solution. The confusion here is understandable. In an effort to move from the Wild West world of shoot-from-the-hip decision making to a more evidence-based model, companies realized that they would need data. As a result, organizations started metering and monitoring every aspect of their businesses. Sales, manufacturing, shipping, costs and whatever else could be captured were all tracked and turned into well-controlled (or not so well-controlled) data. I would argue that what you want and what you need is to turn that data into a story. A story explains the data rather than just exposing it or displaying it. A narrative that gives you context to today’s numbers by exploring the trends and comparisons that you need in order to make sense of it all. The belief that Artificial Intelligence can support the generation of natural language reporting from data is what drove me to help found our company, Narrative Science. I fundamentally believe that a machine can tackle and succeed a...

Description : Which of the following statement(s) is/are FALSE in the context of Relational DBMS ? I. Views in a database system are important because they help with access control by allowing users to see only a particular subset of the data ... (B) III and IV only (C) I, II and III only (D) II, III and IV only Last Answer : (D) II, III and IV only Description : Differentiate between training data and testing data. -AI Class 9th Last Answer : Training Data Testing Data Artificial Intelligence is created primarily from exposure and experience. In order to test the performance of models, they need to be challenged frequently. In order to ... to fit on the training data set and verify for its proper functioning for further generalization. Description : Read the following passages and answer the questions given below each: Trees are useful to man in three very important ways they provide him with wood and other products, they give him shade and they help to prevent droughts ... . . (a) wood and other products. (b) shade (c) fuel (d) all these. Description : A kiosk a) is data organised and presented in a manner that has additional value beyond t he value of the data itself b) combines microscopic electronic components on a single integrated circuit that ... and service d) describes a computer's type, processor, and operating system e) None of these Last Answer : c) is a computer station that provides the public with specific and useful information and service Description : Ho...

Do your employees have the skills to benefit from big data? As Tom Davenport and DJ Patil note in their October Harvard Business Review Companies grappling with big data recognize this need. In Ready and willing to experiment: Managers and business analysts must be able to apply the principles of scientific experimentation to their business. They must know how to construct intelligent hypotheses. They also need to understand the principles of experimental testing and design, including population selection and sampling, in order to evaluate the validity of data analyses. As randomized testing and experimentation become more commonplace in the financial services, retail and pharmaceutical industries, a background in scientific experimental design will be particularly valued. Google’s recruiters know that experimentation and testing are integral parts of their culture and business processes. So job applicants are asked questions such as “how many golf balls would fit in a school bus?” or “how many sewer covers are there in Manhattan?” The point isn’t to find the right answer but to test the applicant’s skills in experimental design, logic and quantitative analysis. Adept at mathematical reasoning: How many of your managers today are really “numerate” — competent in the interpretation and use of numeric data? It’s a skill that’s going to become increasingly critical. VELUX’s Reinhardt explains that “Business users don’t need to be statisticians, but they need to understand the...

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