For that one needs to know the goal, map the business data collection processes, see what internally or externally exists but all in light of certain questions. In what follows, some of the key mistakes with regards to big data analytics are elaborated. Their recognition can help establishing an effective approach in use of big data.
1. The Haystack Syndrome
Late Dr. Eli Goldratt in his book published in early 1990s, entitled Haystack Syndrome – The: Sifting Information Out of the Data Ocean, beautifully explained ahead of its time that a "must" for every manager concerned with meeting the challenges of the 21st century is to see the differences between data, information, and decision-making in a new light, the goal – creating value, making money! The quality of analytics today hangs all into the goal. When Big Data buzz word was not hovering around every mangers head, Eli mentioned all starts with the structure of organization and the understanding what we want to do with the data, posing the right question and underpinning then right the information architecture rather than searching for software. Over quarter of century later, the Haystack Syndrome in the new light of real Big Data suggests failures are due to having no objective, wrong-formulated questions, followed by wrong architecture and structure in the organization. Therefore, one cannot achieve the goal of generating value through Big Data.
2. Crowd mentality
While the word "big data" has already reached top of every search engine yet only a few people really understand them. Without having the “right” people around, following the crowd just because everybody is doing it, brings about more chance of failure than any success. The need for going for big data must be studied very carefully and patiently – there is no plug and play recipe around, no one size fit all is available. And surely, it may not fit every organization, but again, it is about understanding the reason why it should be done in the first place. With understanding of the vision and/or the goal comes the proper planning that helps identify the right people, the right process, and the right technology for big data.
3. Big Data Silhouette
Almost all Big Data appraisals papers suggest a picture where Data Warehouses can nicely and easily be connected; in theory yes but in practice not. This has created in reality a situation where many businesses attempt to organize and build data hubs for various functions. However, a Big Data Silhouette suggests a solid Data pool where the total picture can be solidly seen, where actually, the walls between functions / business units are removed and data flows through for more value generation.
4. Stakeholders-wide Engagement & Change Management
As much any other project, big data value creation requires a good change and project management system in place. Often as any change project, too much of attentions is paid to the technical sides and the human capital in a project is overlooked. Applications of Business Analytics tools, collections of right data, constantly mentoring risks and security issues involved, all must be touched by trained people, they need to be directed and led towards how the new organization operates. Failure to see the importance of change and project management results in unbalanced emphasis on various aspects of a project and lack of acceptance of all stakeholders.
5. Ethical, Legal, Regulatory, or Privacy Compliances
Big Data Analytics bring about various formats of accountabilities. Failure could be due to not recognizing these in design phase by ignoring at the ethical, legal, regulatory, or privacy compliances issues. The massive data collected in various formats and interlinked can easily infringe the peoples and organizations’ right. Disagreements of the laws with certain aspects of a Big Data project may cause splitting and isolating certain "activities” which then may hurt the core to how a company uses its data. The problem can exacerbated when different projects using the very same “activities” – therefore the strategic priorities would be jeopardized.
A new way of doing business
Both the crisis and tech are pushing prices down, resulting in a new way of doing business that is to a certain extent displacing some traditional sectors and making us rethink others. Uber (taxis), Airbnb (hotels), and Spotify (music streaming) are all examples. These companies both have much lower-cost models than those that traditionally predominated their industries and provide great value to customers. These new businesses also exert pressure on prices. In this context, the new consumer has trimmed away all the fat arising from the easy access to money before the crisis, which led to a decline in the value of money and, therefore, more transactions and brands in each market. The old paradigm was management. If you were entrepreneurial and a good manager, you might not be the market leader or have the best brand, but you could survive. Today that is no longer possible.
With the crisis and the collapse of companies, it is not enough to have entrepreneurial DNA and a good head for business management; you need to be unabashedly brilliant. Today’s customer is mature and connected, but also influential, demanding, conscientious, and cautious. Ultimately, all of this leads to market polarization. Many markets have already narrowed, which means we are dealing with either the low-cost logic of competing on price, where the main factor in the customer’s decision is cost, or a high-end range, which may not be premium or luxury, but in which companies compete on value added. What we are no longer seeing is a muddle of brands in the no-man’s land in between. The brands that manage to escape this “valley of death” are the ones that deliver value, either through price, which is the first explanatory factor for a product, or by creating customer value.
The art of brand building
For the American economist Philip Kotler, the art of marketing is the art of brand building; if you are not a brand, you are a commodity, whose sole selling point is price. For most companies, competing on price is not the best option, as in this field there is almost systematically one opponent in every market, and you need major scale to win. Therefore, it is clearly better to heed Professor Kotler’s words and try to build a brand, an endeavor closely related to equity.
There are two optics of brand equity: the customer’s and the company’s. When assessing a brand, customers tend to consider attributes, feelings, sensations, or thoughts. However, brands are also highly profitable assets for companies. The main annual business valuation reports include value breakdowns. While tangible assets (equipment, buildings, vehicles, etc.) account for an average of 36% of a company’s sale price, the brand accounts for 38%. Logically, this percentage varies depending on the firm and industry, but it shows the importance of a brand. Therefore, whether our goal is to sell the company or grow it, the amount of energy we devote to keeping tangible assets alive and valuable as opposed to the brand is crucial. Another increasingly valued asset is human capital, or talent, which accounts for almost 17% of the valuation.
The only way to have a valuable brand, with a market reputation able to protect us from some competition, is to create clear, sustainable customer value. To this end, I propose working with a methodology, or roadmap, that organizes the process within the company with a view to ultimately producing something that will have a name, symbol, or color. The branding model I use draws on the work of leading American and European authors. The first step is to lay the foundations. This refers to what we need to do to develop a strategy, establish a framework, and identify the competitive context of the brand we want to use for the branding strategy. Alignment is essential in order to subsequently define the identity, conduct the internal branding, and move on to the key step of brand delivery. Brand building never ends; it is a cumulative process, and three or four missteps can do incalculable damage in a market as complex as today’s.
XEDGlobal Program Choices for Data Analytics:
Northwestern Kellogg - Leading with Big Data and Analytics: From Insight to Action
Indian Institute of Management, Calcutta - Executive Programme on Business Analytics (EPBA)
S.P. Jain Institute of Management and Research, Mumbai- Big Data Analytics
McComb School of Business- Predictive Analytics