Basic points about Technology & Data
What technology can be considered the most promising and useful for business? Artificial Intelligence, Internet of Things, big data? It depends on the industry specifics of a particular company and the processes that need to be improved, you say. And you will be right. However, does it matter what innovations are introduced into the business to transform it, if the data that IT solutions will work with is not processed in any way and is in a state of chaos? In any case, the company will not get the results that it was counting on customer dissatisfaction, inefficient allocation of resources, inability to notice threats. Garbage in, garbage out, as George Fuechsel, who used GIGO as a training method, said.
The fact that data is one of the most valuable assets of business has already been said a lot. It comes from outside and gathers inside almost every company: it is information about customers, partners, and competitors, information about the work of internal processes, about problems and achievements. However, a Veritas poll found that 33% of the data that companies store is hopelessly outdated or redundant.
In a word, data needs to be effectively managed – only, in this case, it will become an asset that brings real benefits and profits to the business. However, what does “manage” mean? There is a lot of conflicting information in open sources, strange schemes that are more likely to be confused than explained. Data management is a combination of various principles and tools: the first ones help to improve the quality of company information. The second ones help to evaluate their quality, process, and extract insights.
Strategic data management (quality assessment) is called Data Governance (DG), and practical (quality improvement) is called Data Management (DM). The fact that these concepts are not interchangeable (even though both translate as “data management”) was one of the first to speak for Forrester chief analyst Michele Goetz. To make it easier for the audience to feel the difference between them, specialists from one of the foreign IT providers compared information processing and cooking. Therefore, Data Management is kitchen cabinets that store products and all household appliances, and Data Governance – recipes/instructions that help to correctly dispose of a large number of ingredients and use the equipment to make a delicious dish out of them.
In other words, competent Data Management is impossible without a well-thought-out data management strategy. DM tools (for example, ETL systems or MDM solutions) are standardized: they can be customized to work with specific information and processes. DG, on the contrary, is a unique plan that takes into account the particular company (although some ten years ago Data Governance was inextricably linked with industrial models, whether it be IBM BDW or Sybase IWS).
Working with data: basic principles
Each company should have its Data Governance. According to Vimal Vel, vice president of Dun & Bradstreet (a provider of data solutions), many of their clients are inspired by the idea of Data Governance and immediately begin to develop a strategy without paying attention to the preliminary stage – preparation. Meanwhile, one cannot do without it: first, you need to understand what stage the business is now and what you would like to come to after a certain time. Most of the data that is being worked on, as part of the strategy should be related to business performance.
Surveys show that many companies do not have a clear understanding of what to consider when strategically managing data. In particular, only 48% of survey participants conducted by First San Francisco Partners had a program to work with their information. Meanwhile, there are several understandable principles on which the Data Governance strategy is based.
1. Make your data accessible
Not a single “smart” system will work in full force if it does not have access to the necessary information. However, here you must not forget about confidentiality: when drawing up a DG strategy, it is important to understand what and how specific specialists of the company can explore and what data to see as a structure or “stars.”
For example, if we are talking about a bank, then the HR department and management, of course, need to provide access to personnel information, for example, information on salaries and travel expenses. However, the card system, which contains information on the income and expenses of specialists, should be well protected – it is hardly needed to work with the company’s data management strategy.
2. Users must work with consistent data
The situation when the employees of the commercial department work with one data and the specialists of the sales department with other information, can lead to the fact that as a result, conclusions about similar processes will be completely different. With such conflicting results, it will be difficult for business management to understand who is right and whose recommendations should be followed.
Therefore, employees must work with consistent data from a single source. Now companies have two options. The first is to set up a transparent process for exchanging information between different repositories. The second is to place all the information in a single repository. For example, a Data Lake: it allows you to store both structured and unstructured data of various formats. It makes it easier for those who, for example, interact not only with documents but also files in audio – or video format (by them we mean the metadata of these files and transcription).
All the same, banks felt the benefit of creating centralized DWH/Data Lake. For example, not all of them had a so-called communication matrix with their customers, thanks to which you can understand: how many and to whom SMS or e-mails were sent, calls with which offers have already been made, and what kind of response. After placing all the information in a single repository and building an end-to-end communication matrix, banks were able to reduce the number of duplicate or uninteresting messages for certain clients.
3. Understand what to collect and what to throw away
Do not collect useless “garbage”: the use of irrelevant or hopelessly outdated information for managerial decision-making is unlikely to increase the company’s competitiveness.
A data management strategy suggests that a place in the sun – in a repository – should only receive fresh and relevant information. As we remember, the plan is always individual. Therefore, for each company, the compliance criteria will be different. Do not turn your data lake into a swamp and set up processes to get rid of unnecessary information in time.
Of course, permanently deleting data that does not meet the criteria is optional. You can put them in the archive, but first, it’s important to describe everything carefully. If archival information is ever needed, finding it without a detailed description will be difficult. It is essential for owners of websites like homemakerguide.com to understand this aspect to make most of out of their business.
4. Keep your data safe
Any system can ever fail, and all the information accumulated in it will disappear. Hope for IT, but don’t forget about backups – another important principle.
5. Take care of security
In recent years, the rules for handling data, including personal data, have become stricter – non-compliance can lead to fines and damage the company’s reputation. Data Governance assumes that the business has clear guidelines on how to protect information from theft or unauthorized use. Even if the company is very small, you should not think that it is uninteresting for cybercriminals – on the contrary, hacking its system will be easier than getting into the storage of a large corporation.
Another important question: what should a company do if a contractor, such as a system integrator, is working on Data Governance? Draw up regulations for the issuance of data, sign the NDA? Our experience shows that even at the stage of creating the accounting system of the company, it is worth marking the data to avoid confusion and misunderstanding. It is important for management to understand what information is classified and in what cases. As a result, when choosing software for building a strategy for working with data, the customer’s team and the contractor’s team will find it easier. They will receive some data unchanged and some in the form of the same “stars.”
In other words, the data management strategy can be compared with financial management: the latter must be kept in order and maintained through audit and various control tools. It is equally important to ensure the safety of funds. Data is the same asset and sometimes even more valuable in the modern world. Therefore, the same rules apply to it: like competent financial management, Data Governance helps reduce errors, increase business efficiency, and clearly understand what results you can reach further.
Author’s Bio: Randolph Ray is one of the co-founders o MSM Data Research and Management Company. He writes about business operations, economic goals, and data research strategies. In his free time, Randolph enjoys charcoal painting and playing chess with his son as well as and submitting articles for homemakerguide.com and various other different sites.