McKinsey teaches you four principles to hold "big data" opportunities

At the beginning of the year, although a variety of keywords continued to appear in domestic and foreign business, technology trends in the prediction of the article, "Big Data" is not an exception to the attention. Companies are already working hard to develop strategies by using large-scale data collection and analysis, which powerfully reflects the enormous potential of large data. The acquisition and analysis of large data is rapidly becoming a new competition area for enterprises to acquire differentiated competitive advantages. Companies such as Amazon.com, Google and Netflix are doing well, while others are on the chase. In fact, in recent months, pharmaceuticals, retailing, telecoms, insurance and other industries have been forging ahead with big data strategies. Their largesse suggests that the industry is making a new strategy for big data, and that executives such as CEOs face the challenge of blocking big data initiatives. Based on relevant experience, we have summed up four principles, hoping to help executives capture large data development potential.

1. Measuring Opportunities and challenges

If executives are aware of the urgency of coping with the industry's profitability challenges, they can often contribute to their big data strategies. In AstraZeneca, for example, executives see the power of real-life data, such as the underwriting of medical bills, to allow pharmaceutical companies ' clients to assess the effectiveness of their medicines (see "Big Data" in AstraZeneca).

In one of the retailer cases that we've studied, big data is a hard battle to capture market share. The company's strategy has always focused on major challenges facing rivals, but a competitive threat from the web is eroding revenues and profits. The core of this threat comes from the ability of competitors to collect and analyze consumer psychology, and to offer advice to millions of of customers, a capability that has discounted the retailer's ability to sell. At the same time, the competitor is forming a platform on which vendors can use industry-aggregated open price data to sell excess inventory to help suppliers determine discount margins. To this end, the retailer's board wants to know whether it can also use its own information resources to address these challenges.

The challenges and opportunities posed by the data may be more subtle. For example, when a European telecoms firm uses innovative product bindings to increase market share, large data analysis is seen as a milestone in the company's revitalization. The company's executives believe that the company's new strengths can be shaped by pinpointing the points through which sales are driven and by studying consumer behavior to determine which factors define their brand or product choices. This requires the ability to interpret two large and growing information: Web search data and real-time information, that is, the consumer's assessment of the company's products and services, shared by social tools and other network channels.

2. Identifying large data sources and gaps

To develop a large data strategy, it is of course necessary to study what type of information the enterprise needs and what capabilities it needs. At this stage, business executives should thoroughly evaluate all relevant internal and external data. Auditors should also consider the availability of analytical talent and potential partnerships that might help to fill the gaps. Such audits not only have a real understanding of enterprise capabilities and needs but also inspire inspiration-for example, an executive discovers "data gems" hidden in certain business units, or recognizes the importance of creating value through proper collaboration.

Retailers ' audits focus on internal data, often with untapped value. Such data, including product return rates, licensing and customer complaints, are valuable information that covers customer consumption habits and preferences. The audit also found an impediment: No data that was combined with customer identification data, or data that was standardized enough to be shared within and outside the company. As a result, such information is rarely used for marketing analysis, nor is it possible to support sales reps interacting with customers, or supply chain executives serving suppliers. Fortunately, the audit found that there is a team that can help solve these problems: the internal data analyst, the team's independent work has not been fully utilized.

In the case of European telecoms providers, the question focuses on how to use consumer online discussion information about the company and its products, including millions of related microblogging postings, social media interactions, search keywords, direct brand comparisons, and customer feedback posted on the site. Recognizing the importance of the work and discovering the company's lack of quantitative economics and analytical skills, the CEO of the telecoms company hired a team of "brainstorming" teams of external analysts with relevant qualifications.

3. Consensus on strategic options

Once the available opportunities and resources are found, many enterprises will immediately enter the implementation planning phase, but this is wrong. The data strategy is inextricably linked to the enterprise's overall strategy and requires careful planning in order to achieve the desired results. This requires front-line personnel to be able to use advanced data analysis tools, or to collect data and recruit analysts to gain a first-order advantage.

It is also important to examine large data in the context of determining the priority of competitive strategies. When the CEO pays close attention to how to promote the data orientation of the company's sales and marketing functions, it finds it necessary to replace the internal strategic leaders with the key data suppliers and invest huge sums of money to train the analysts.

Before removing big data from the company's strategic focus list, ask yourself if you have ever thought about the potential value of this long-term strategy, and whether you've thought about what your competitors are doing when you're hesitate. In the case of these retailers, executives believe that the goal of a big data strategy should be to create an information network that provides the scope for the entire company's data-sharing and analysis work. However, he did not promote the initiative throughout the company, because the retailer's corporate culture is often the advocacy of business unit level innovation. As a result, he hired a technology and business executive to implement a study that spanned a number of key business units, starting with 80 potentially large data projects, and then sorting each project according to net present value and comparing it to the company's strategic goal one by one.

The retailer's first project was to overhaul its overly decentralized customer relationship management (CRM) system and create a unified set of data sources for executives to use in a variety of ways. In a pilot project, for example, the company made it easier to obtain inventory data, customer information and product information through the use of tablet equipment by sales staff to help it expand its sales. The second initiative is to use Internet operators to create virtual Third-party Web sites. By linking the retailer and the vendor's inventory system with algorithms, investigating market prices, and pre-set discounts, the initiative effectively fights the competitor's Third-party sales strategy and increases sales and vendor commissions.

In the case of telecommunications, a cross-functional executive committee was formed to oversee the analysis team and ensure that it matched the company strategy. The panel asked the analysis team to focus on addressing two issues: "What is the competitiveness of our products in the minds of the users when they decide to buy?" What factors directly affect purchase? How do we position these factors when communicating with users?

The analysis team then created a targeted "data mix" for quick analysis of what could be done, such as sports and other golden television programmes, as important differences in the decision to buy. If companies weaken voice phone marketing, customers tend to buy "triple" services (TV, high-speed Internet, voice telephony). This is in contrast to the consumer tendencies reflected in traditional market research interviews. In addition, for business executives, the analysis suggests a need for a larger strategic approach (and detailed quantitative indicators): The use of mobile phones as a category fourth service, the formation of "Si one" products to consumers.

(Responsible editor: The good of the Legacy)