Why does big data matter, in a general sense? It gives you a more comprehensive view. It enables you to operate more intelligently and drive better results with your resources by improving your decision-making and getting a stronger grasp of customers and employees alike.
Big data may simply seem to be a way to build revenue (since it allows you to better zero in on customer needs), but its use is much broader – with one key application now being cybersecurity. Big data analytics allow you to determine your core risks, pointing to the compromises that are likeliest to occur.
Big data is not some kind of optional add-on but a vital component of the modern enterprise. Through details on where attackers are located and incorporation of cognitive computing, this technology helps you properly safeguard your systems.
Ways data is valuable
There are various ways in which data has value to business:
Automation. Consider the very real and calculable value of task automation. AI, robotic process automation (RPA), chatbots, and similar technologies allow for automation of repetitive chores. When you consider the value of automation, you are thinking in terms of how much it is worth to have that person working on other, more complex tasks as they are freed by the automation.
Direct value. You want to get value out of your data directly. Deloitte managing director David Schatsky noted that you want to consider key questions such as the amount of data you have, the extent to which you can access it, and whether you will be able to use it for your intended purposes. You can simply look at how data is being priced by your competitors to get a ballpark sense of direct value. However, you may need to conduct a fair amount of testing yourself to figure out what the true market value really is. Don’t worry if this process does not come naturally. An organization that is digitally native will be likelier to prioritize its data and know how much value it has for them; after all, they are fundamentally focused on using data to grow their businesses.
Risk-of-loss value. Think about information the same way you think about losing a good friend or important business contact. In many cases, we only appreciate what we have when it’s gone, but you have much better foresight if you consider your data’s risk-of-loss value – the economic toll it would bring if you could not access or use it. Similarly put a dollar amount on the value to your organization of data not being corrupted or stolen; i.e., how much should it really be worth to you to keep data integrity high and not undergo a breach? Think about a breach: you could have to deal with lawsuits, fines from government agencies, and lost opportunity cost alongside actual cost. Also keep in mind that you could have a nightmare situation in which your costs exceed the amount of your cybersecurity insurance policy – so you think you are prepared but get blindsided by expenses nonetheless.
Algorithmic value. Data allows you to continually improve your algorithms. That creates value in the sense of identifying the most powerful user recommendations because we have all experienced system recommendations that were meaningful and ones that were not, so increasing relevance is critical. It is now considered a standard best practice that you can better upsell and cross-sell when you have integrated product recommendations for customers to add. A central concern with algorithms is your algorithmic value model. The data that you feed into it should be as extensive and accurate as possible; for example, you might have data on destruction from a natural disaster such as the flash flooding in Jakarta, Indonesia. You get a sense of economic damage via as thorough a data set as possible on damaged buildings and infrastructure – so the quality and scope of your data set will determine how good the algorithm is.
Why know data values?
You want to prioritize data. You want to understand the diverse ways it has value. It also helps to understand exactly how valuable certain data is to you. Data valuation – sound, accurate valuation – is critical for three primary reasons:
Easier mergers & acquisitions – When mergers and acquisitions occur, the stockholders may lose out if the valuation of data assets is incorrect. Data valuations can help to bolster shareholder communication and transparency while allowing for stronger terms negotiation during bankruptcies, M&As, and initial public offerings. For instance, an organization that does not understand how much its data is worth will not understand how much a potential buyer could benefit from it. Part of what creates confusion related to data valuation is that you cannot capitalize data per generally accepted accounting practices (GAAP). Since that is the case, there is great disparity between the market value and book value of organizations.
Better direct monetization efforts – As indicated above, direct value is an obvious point of focus. You can make data more valuable to your organization by either marketing data products or selling data to outside organizations. If you do not understand how much your information is worth, you will not know what to charge for it. Part of what is compelling to companies considering this direction is that you can garner substantial earnings from indirect monetization. Firms remain skeptical about sharing data with outside parties regardless the potential benefits, since there are privacy, security and compliance issues involved.
Deeper internal investment knowledge – Understanding the value of your various forms of data will allow you to better figure out where to put your money and to focus your strategy. It is often challenging for firms to figure out how to frame their IT costs in terms of business value (which is really necessary to justify cost), and that is particularly true with data systems. In fact, polls show that among data warehousing projects, only 30% to 50% create value. You will get a stronger sense of areas that could use greater expenditure and places of potential savings when you have a firm grasp of the relationship between your data and business value.
You can greatly enhance the relationship between business and IT leadership by learning how to properly communicate the value of data. The insight into data value that you glean from assessing it will lead to CFOs being willing to invest additional money, which in turn can produce more positive results.
Steps to better big data management
Strategies to improve your approach to big data management and analysis can include the following:
Step 1 – Focus on improving your retail operations.
The ways that shoppers will behave, which in turn tells you roughly how they will act on a site, is being bolstered through innovations in machine learning, AI, and data science. Retailers benefit from this data because it helps them determine what products they must have in stock to keep their sales high and their returns low. It also helps to guide advertising campaigns and promotions. In these ways, sharpening your data management practices can lead to greater business value.
Step 2 – Find and select unified platforms.
You want to be able to interpret and integrate your data as meaningfully as possible. You want environments that can draw on many diverse sources, gathering information from many types of systems, of different formats, and from different periods of time, brining it all together into a coherent whole. Only by understanding all of the data at your disposal holistically and as part of this fabric can you leverage true real-time insight. You should also have sophisticated enough capabilities to partition data as you go into what you need and don’t for certain applications, with baked-in agility.
Step 3 – Move away from reliance on the physical environment.
Better data management is also about moving away from scenarios in which data is printed and evaluated as hard copies. IT leadership can instead use an automation platform to send out reports to all authorized people, and then allows you to view the reports there.
Step 4 – Empower yourself with business analytics.
You will only realize the promise of big data and see competitive gains from it if you are getting the best possible numbers from business analytics engines. For scenarios in which you are analyzing batch data and real-time data concurrently, you want to be blending together big data with complementary technologies such as AI, machine learning, real-time analytics, and predictive analytics. You can truly leverage the value of your incoming data through real-time analysis, allowing you to make key decisions on business processes (including transactions).
Step 5 – Find ways to get rid of bottlenecks.
You really want simplicity, because if a data management process has too many parts, you will likelier experience delays. One company that realized the value of its big data, highlighted in a story in Big Data Made Simple, is the top Iraq telecomm firm, AsiaCell. When AsiaCell started paying more attention to big data management, they realized that they sometimes unnecessarily copied data and frequently lost it because they did not have processes that were established and defined.
Step 6 – Integrate cloud into your approach.
Cloud is relatively easy to deploy (without having to worry about setting up hardware, for instance), but you want to avoid common mistakes, and you need a plan. After all, you want to move rapidly for the greatest possible impact (rather than losing a lot of energy in the analysis as you consider transition and review providers). To achieve this end, many organizations are shifting huge amounts of their infrastructure to cloud, often doing so in conjunction with containerization tools such as Docker (for easier portability, etc.). Companies will often containerize in a cloud infrastructure, and then reference the apps with other ones inside the same ecosystem.
Deriving full value from your data
Incredibly, the report Big & Fast Data: The Rise of Insight-Driven Business said nearly two-thirds of IT and business executives (65%) believed they could not compete if they did not adopt big data solutions. Well, there you have it: more and more of us agree that big data is critical to success. That being true, we must then assume that taking the most refined and sophisticated approach possible to analysis is worthwhile. TSS Big Data consulting / analytics services allow you to efficiently harness, access, and analyze your vast amounts of data so you can take action quickly and intelligently. See our approach.