Deep Data

https://channels.theinnovationenterprise.com/articles/the-difference-between-big-data-and-deep-data

Big data has become an important topic for nearly every industry. The ability to study and analyze large sections of information to find patterns and trends is an invaluable tool in medicine, business and everything in between. Employing analytics, the root of big data, in your business can lead to advances and discoveries that you might not see otherwise.

When it comes down to it, though, big data isn’t really a new concept. It’s simply taking data already available and looking at it in a different way. Deep data, on the other hand, may be the real tool that you need to change the world, or at least your industry.

What is big Data?

Big data is an amalgamation of all of the data collected by a business. The specifics will vary by industry, but generally, its information like customer or client names and contact information and other data collected over a business day. Depending on the side of the business, this can be mind-boggling amounts of information, much more than it would be possible for a regular human to go through.

Businesses can employ predictive analytics to help sift through the data to find patterns and trends, but much of the information is often useless or redundant.

What is Deep Data?

Deep data is, in essence, taking the data gathered on a daily basis and pairing it with industry experts who have in-depth knowledge of the area. We’re talking exabytes or petabytes of data — much more than what could fit on a standard computer or external storage drive. Deep data pares down that massive amount of information into useful sections, excluding information that might be redundant or otherwise unusable.

What’s the Difference?

Big data and deep data are inherently similar, in that they both utilize the mass of information that’s collected every single day by businesses around the world. Companies can pair this data with analytics and use it to help predict industry trends or changes, or to decide what departments need to be investments or reductions in the coming year. So how are the two types of data gathering different?

The key is in the data analyzed.

• Big data collects everything, down to the last insignificant zip code or middle initial. Trends can be found this mass of data, but it’s much harder to determine what is useful and what is just junk code.
• Deep data, on the other hand, looks for specific information to help predict trends or make other calculations.

If you want to predict which products are going to sell the best during the next calendar year, for example, you wouldn’t necessarily be looking at your customer’s location, especially if you sell online. Instead, you look at data like sales numbers and products information to make predictions. That’s the essence of deep data.

Deep data analysis applies to medicine and other similar fields as well. Focusing on one specific demographic, such as age, weight, gender or race, can help make trail participant searches much more streamlined and increase the accuracy and efficacy of drug or treatment trials.

Which one do you need?

Of the two options, is big data or deep data the best option for your business? That will depend on the kind of business that you run, the industry that you’re in, and the type of data you’re collecting.

In general, though, when searching for specific trends or targeting individual pieces of information, deep data is going to be your best option. It allows you to eliminate useless or redundant pieces of data while retaining the important information that will benefit you and your company.

Big data and deep data are still both very useful techniques for any type of business. A data consulting firm can help you determine the best techniques to gather and process your data. We are entering the age of big data, and it won’t be long before big data or deep data becomes a necessity rather than an option.

In the era of Big Data, businesses across all industries struggle to efficiently and effectively manage data overload. Many organizations do not know how to pinpoint and extract the value embedded within the heaps of data available to them, and consequently find themselves stuck in the "data hoarding" trap – capturing every piece of available information, rather than focusing on the data that is providing the greatest business value.

Instead of just thinking "big" when it comes to data, companies need to start thinking "deep." The Deep Data framework is based on the premise that a small number of information-rich data streams, when leveraged properly, can yield greater business value at lower cost than vast volumes of data. For example, organizations can use the Deep Data framework to better understand a customer's behavior and provide actionable, scalable insights that simultaneously improve customer engagement and drive economic value of that company's data investment.

What do you need to know to effectively implement a Deep Data strategy? In this slideshow, Badri Raghavan, CTO and chief data scientist at FirstFuel, has outlined valuable insight organizations should consider.

Defining Deep Data: What It Is and How to Use It

A Closer Look at Deep Data

Click through for insights into how organizations can leverage Deep Data to better understand customers and improve business strategy, as identified by Badri Raghavan, CTO and chief data scientist at FirstFuel.

Deep Data ­ A Brief History

What we now call the Deep Data framework first emerged with the financial service industry's FICO score in the 1980s. The FICO score was based on a few rich sources of financial history (typically credit card activity) to determine the credit-worthiness of a potential borrower. This approach is now being seen across industries including health care, retail and energy. For example, in the energy sector, utilities are tapping data-driven technologies to unlock insights into customer usage and create more customized, valuable customer engagement.

Develop a Data Strategy

To effectively implement a Deep Data approach, companies must first develop a plan for leveraging the data and outline the desired results. Defining goals and working back toward the data sources and analytics that unlock value present in the data allows companies to determine the right pieces of data to capture rather than risk drowning in data with no identified value "to be used later." Smart strategies strategically unite three core elements – domain expertise, data science and the right IT infrastructure.

Planning for Data Security and Privacy

Any organization looking to implement a data-driven technology has to have the right IT infrastructure in place and ensure that the appropriate customer privacy and information security measures are taken to protect both the company and its customers. This will help companies avoid data breaches and their unpleasant consequences as much as possible. Defining data outcome targets before creating the IT infrastructure allows businesses to invest at the appropriate level so that they aren't left with either insufficient processing or storage capacity or with an expensive system that is largely unused.

Bring in the Data Scientist

When formulating a Deep Data strategy, businesses should take advantage of the emerging breed of data science specialists. A data scientist not only has the specialized mathematical, programming and statistical knowledge to help companies extract business value from the data, s/he also provides valuable insight on the best strategy to help companies meet their goals.

Adopt a Data-Centric View

A final key element in implementing a data strategy is to encourage adoption of a data-centric view at every level of the organization. As part of this, it is important to democratize access to data. This allows different individuals or departments to leverage the data for uses that best fit with their needs, while also enabling new avenues for innovation because of collaboration across the company.

Definition - What does Deep Analytics mean? Deep analytics is a process applied in data mining that analyzes, extracts and organizes large amounts of data in a form that is acceptable, useful and beneficial for an organization, individual or analytics software application.

Deep analytics retrieves targeted information from data stores through data processing methodologies.

Techopedia explains Deep Analytics Deep analytics generally extracts information from data sets that are hosted on a complex and distributed architecture, with the implementation of data analysis algorithms and techniques. The deep analytics process requires operation on a huge amount of data, typically in petabytes and exabytes. The data analysis workflow is spread out across a number of server or computing nodes to speed up the process.

Deep analytics is often coupled with or part of business intelligence or data mining applications, which apply query-based search mechanisms to data stores to analyze and extract the best data match, and convert that information into specialized reports, charts and graphs.