The Big Bang of Data

Determining the Business Demand for Data, Information and Analytics

An Explosion of Data

As technology advances, the amount of data that is being created is growing at a rapid pace. Where throughout the 1970s, ’80s and ’90s most data that was being collected was structured and placed in databases, emerging technologies like the internet, mobile phones and smart devices, are generating more data than ever before. In 2013, IBM wrote that each day 2.5 quintillion bytes are created, and it is estimated that 40 Zettabytes of data will have been created by 2020. But just as the Volume of Data is rapidly increasing, so is the Variety of Data. Everything from emails, tweets, images, video and music, are now digital and being stored not just on local devices but more and more in the Cloud. Asking for new types of ways to store both structured and unstructured data, to be readily available across devices and around the world. But maybe the most interesting is the Velocity in which we now rely on the Data that is being generated. Where in the past businesses would be restricted to when the data is available, which could take hours or sometimes even days, nowadays real-time data is necessary in different industries, like for example Finance, Marketing and Technology.

Taking Control of Analytics

But as the Volume, Variety and Velocity of Data is increasing, so is the need for analytics that is able to handle it. Even though most companies recognize the importance of collecting data in each process ranging from their sales process to their financial process, many companies are mostly overwhelmed by the amount of data, and are more often clueless about how they can effectively analyze it or transform the data into information that is current enough to act up on. To make matters even more complicated, is the fact that each part of the business relies on information that is derived out of this data, which has a big impact on the business. From sales that is managing their pipelines, to marketing that is trying to figure out if the website is producing the desired results, to up-to-date financial information and even human resources which is tracking employee satisfaction.

The Importance of Analytics

Businesses cannot underestimate the importance of their analytics initiatives. While enterprises still need leaders and decision-makers with intuition, they depend on data to validate their intuitions. In this sense, data becomes a strategic guide that helps executives see patterns they might not otherwise notice. A study from Bain found that enterprises with the most advanced analytics capabilities outperformed competitors by wide margins, with the leaders showing these results:

  • Twice as likely to be in the top quartile of financial performance within their industries
  • Five times as likely to make decisions much faster than market peers
  • Three times as likely to execute decisions as intended
  • Twice as likely to use data very frequently when making decisions

Analytics Challenges

As more companies are using Analytics, the types of analytics is also growing in complexity. Advanced Analytics is expanding to include predictive analytics, data visualization, and data discovery. This now calls for a new breed of data-savvy people who are able to develop complicated analytics models, but also understand the domain of the business as well as present the results in a matter that is useful for business people, otherwise known as Data Scientist. With more companies recognizing that they need Data Scientist to lead their analytical endeavors, the open positions are far more than the actual availability of Data Scientists, leading to an increase in salaries and employee churn.

Analytics Strategy

As we established that the explosion of data and the need to turn that data into information is crucial for businesses in today’s economy, the need for an analytics strategy becomes even more apparent. A report published by Forrester describes why BI analytics is so critical:

  • Many business decisions remain based on intuitive hunches, not facts
  • Analytics lessens the discontinuity between intuition and factual decision-making
  • Competitive differentiation can be achieved by more sophisticated data usage
  • Big Data enables new use cases but will require analytics to take full advantage of its potential

To make the most out of the power of analytics, an enterprise needs a strategy based on how its business people interact with and use data. An Analytics Strategy may include:

  • Designing a data architecture that enables reporting, analytics, predictive modeling, and self-service BI
  • Architecting a BI portfolio
  • Architecting solutions with data discovery, data visualization, and in-memory BI
  • Enabling operation and analytical BI
  • Designing and implementing analytical sandboxes and hubs
  • Creating data and analytical governance programs
  • Creating shared BI metadata environments

But most of all an Analytics Strategy needs to have an objective, which is derived from the business objectives and should support the business people with the information to achieve those objectives.

The Difference Between Data and Information

Even though the words Data and Information are often used to say the same thing, there is actually a big difference between the two. Data is raw, random and unorganized, where as Information is data that has been organized, structured and processed. To give a more specific example, an online payment has a lot of details including ranging from timestamps, card type, merchant id and tens of other fields, the raw data that is send back and forth between the acquirer and issuer is stored in databases within the platform. It isn’t until the steps are taken to move the data into the ETL (extract, transform and load) system, that is transformed into information. The final step is turning the information into Knowledge. Business people consume the information in reports to gain knowledge that helps them make informed business decisions.

The Role of BI in Creating Actionable Information

Business Intelligence (BI) turns data into “actionable” information — information that is useful to the business and helps it gain knowledge. As business is changing at a rapid pace, the need for tools that can provide business people with information that is able to produce actionable insights, is more important than ever. Where up until ten years ago, most of the data was mostly in a structured (SQL) database, today BI tools need to be able to handle all sorts of data (structured, unstructured and semi-structured) and turn it into information. Also the way that the information is delivered to the end-user has changed throughout the years. Business people are now more mobile than ever, instead of accessing the information through a desktop or laptop, the expectations of users are that the BI tool is able to support mobile devices like Smartphones and tablets.

The difference between giving database access to a business person and providing BI is the difference in objective. Data on its own doesn’t provide any insights, but with a strategy to turn data into information and providing the insights necessary to make decisions to achieve the company’s objectives, business persons can validate their opinions and ensure that the business is moving towards the goals it has created.

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