The Illusion of Big Data

    Is big data really an objective urgent problem for business?
    Maybe this is just a beautiful marketing move by developers of powerful computers and digital data storage and processing products.
    Maybe this is just an attractive advertisement for market research consultants and customer behavior models.
    Or maybe this is just a fashion trend in the field of total monitoring of market entities and predicting their reactions.

    Perhaps there is no "big" data, but there is a big illusion that somehow it will be possible to collect such a huge array of digital information, process it in some magical way and get answers to all the questions that concern the businessman.

    Shot from the movie “Minority Report” by Stephen Spielberg based on the novel by Philip Dick (2002 - 20th Century Fox, DreamWorks SKG).

    ... resource

    Big data is essentially an analytics resource. This is a resource for people doing research and decision making. And like any resource, big data without the ability, knowledge and technology to use it does not work. Someone calls this skill “data mining” - similar to mining, focusing on deep penetration and complexity. Someone calls such a skill “business intelligence” - showing how important the “mental” component is in this process. Someone will like the name “great analytics”.

    But it is known from theory and practice that even the presence of a resource in large quantities does not mean its successful and effective use. Sometimes the excess amount of the resource allows you to build a business model, not on its deep processing into a specific set of products, but on simple packaging and implementation in raw form. Why look for additional options when, without excessive efforts, you can simply sell your raw resource.

    ... a high level of management is needed

    Big data, as an information category, has one feature in contrast to material resources. To use them , a truly high level of organization of business objects and company business processes is required . Without such a level of training, without the presence of a certain qualification from the business, the purchase (or collection) of big data will be of low efficiency. So low that it does not justify the funds invested in them.

    Why should a business spend money on big data if the business layer of management (decision-making) based on analytics has not been created? Absolutely right - there is no need. To one degree or another, those companies come to this that started using big data without adopting analytical technologies into the control loop, the technician of making prepared analytically decisions and which, by and large, are not ready for changes. Such market players will sooner or later abandon big data. Especially the question will be acute when there is increased competition for financial resources within the business.

    Today, the big data market has focused on information technology. It’s clear and nice that big data tools are being developed. But the intensive growth of information networks and the improvement of information technologies removes barriers in computing power. This will force advanced ambitious businesses to reconsider their current passion and shift their focus to new effective methods, tools, management technologies based on knowledge and training.

    Actually, when big data is presented, often it comes to the possibilities of their storage, transportation and processing. The search technology of Internet giants is a prime example of how big data is delivered to businesses. Search algorithms are the most powerful processing of huge growing volumes of information. They are constantly in the process of optimizing, improving the performance of indexing and structuring information. But after all, behind the search technologies on the network are not only big data. Behind them are teams of analysts who possess high-tech knowledge in subject areas.

    Therefore, a reasonable policy for using big data is to build a data analysis team, but not the exclusive alignment of servers, clouds, data mining systems, machine learning, etc.

    ... data mining

    It is worth noting that the definition of “data mining” is not very indicative. It paints a somewhat simplified picture of reality: there are "priceless deposits" of heterogeneous and mixed data, and a professional (or tool) takes and "digs out" these data from those data that, with the "penetrating" look of the manager, open his eyes to everything that happens and he suddenly the righteous thought of the hidden reserves of the business model dawns.

    Miracles do not happen in big data either.In order to get valuable information from some storage, you need to put it there first, then extract, process and visualize it. The emphasis is not correctly shifted to extracting information from the repository, leaving out of focus such things as collecting (receiving) data, structuring data, packing data into the repository, checking the quality of data, organizing the data analysis process, decision-making problems based on big data analysis and much more .

    In addition, even for simple data mining, the correct goal setting will not hurt . Without proper goal setting, anything can come out, not a meaningful result. Let this goal be expressed in the form of a hypothesis or questions, in the form of a problem situation or numerical indicators.

    Any data has context and metadata.that significantly limit their use in certain situations. If the context condition for the task is not specified, the analyst is not able to make a decision on data mining and on the compliance of the data of the task.

    ... time lag

    Despite the efforts of the business to reduce the time from taking information about its condition to the decision to change this state, there are objective reasons for the insurmountable time lag .

    The delay between making a decision and changing the state of the business in accordance with the decision made can also be quite significant. Processes and objects are being rebuilt, interaction is changing, employee behavior is being adjusted, the environment is being adjusted. Therefore, any data and even big data is always data about the past. But management wants to make decisions on their basis for the future . The main thing here is not to overestimate the capabilities of big data and analytics.

    ... external and internal

    One misconception regarding big data is that it is predominantly external to the business. It is believed that big data is data on customers (their behavior), data on competitors, data on various factors of business existence (political, social, cultural), data on markets and consumer trends, data on activity of other businesses. This is partly true.

    But big business data from external sources is linked to internal state data, and are linked strictly and contextually. This is extremely necessary to jointly assess the well-being of the business model and the external environment. Internal data can also be large and powerful for great effective analytics. After all, the answer to the question of what to do to management to rectify the situation can give only internal data .

    ... big or not so

    Another illusion that can interfere with business is that professional productive analytics is based only on big data. There is a real opportunity and experience, combined with the talent of some experts, to propose solutions within the framework of traditional volumes of internal data, especially when it comes to obvious or typical problems in the business model.

    It is impossible to deny the great importance of collecting and analyzing big data for business development. Big data is especially important for a distributed and information-active business. Perhaps big data is the only effective tool to keep abreast of all matters for large corporations and associations with an extensive network of business units. Small and medium-sized businesses can also benefit from big data, especially in cooperation with large companies and communities.

    But you cannot substitute big data for solving pressing problems.It is better to consider them as a direction that supports the central business strategy and allows you to keep abreast of what has happened, is happening and partially predict the development of the situation in the future. But if the business does not have an intelligible strategy and if the business model is seen as primitive and confusing, then no big data is able to help even passive development. Some managers, realizing for themselves the lack of need for big data and not ready for the changes that they promise, do not try to initiate work with them - this is also an example of reasonable reasonable behavior.

    ... as a way to think

    No matter how hard we try, big data is not able to solve all the problems. With the help of great analytics, you can’t build an effective business model. But they can still help optimize it within the framework of the chosen strategy.

    The "magic" of big data, which is somewhat aloof from general attention, is an obvious and reasonable way to reflect on the business and look for ways to improve it . Indeed, a big data project improves the business, and not so much because of the value of some arrays of information, but because management begins to look at its business model from a critical point of view , including based on given information indicators and indicators.

    If the management is closely interested in big analytics, it means that it wants to understand more about its company and this is the beginning of business optimization.

    Instead of big data, you can choose another means of business development, for example, marketing research, statistical calculations, economic and mathematical modeling. The result will be different, but the work aimed at "understanding" the business model will be started and will undoubtedly give some, but more often - a positive effect. Unless, of course, it is carried out objectively, reasonably, professionally and taking into account the influencing factors.

    ... under the big data brand

    Some companies have accumulated a resource - data , while others have developed powerful software and hardware IT solutions . They will try to bring this resource and these solutions under one or another “marketing sauce” to the table of businessmen and earn “good tips”.

    Active marketing and sophisticated marketing, polite consultants and cheerful client events richly “seasoned” with a beautiful brand and impressive terminology will be used. They will talk about building reliable systems for processing absolutely unstructured data, about excellent algorithms for constructing multi-level graphs of information, about high-speed samples on artificial intelligence, about self-learning neural network mechanisms.

    Do not take a word.Ask for clarifications, clarifications, demos, documentation, independent expert opinions, customer reviews, stress tests, a reasonable free trial period.

    Judge for yourself, even the Big Data brand looks advantageous.
    Firstly, the name has the word " big ", which means it is something good, positive, profitable, impressive, convincing, valuable.
    Secondly, the word “ data ”, as it were, indicates something correct, intellectual, innovative, effective, orderly.

    The very essence of the illusion of big data comes from its name.
    It seems that having big data, the business solves the issues of the highest (big) order onprofessional (large) level. And the more data is accumulated, the more efficient and faster will be resolved increasingly complex issues.

    Resist the illusions - big data is not always able to do what the business dreams of.

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