How to successfully develop large enterprise data strategy

As a http://www.aliyun.com/zixun/aggregation/9032.html "> Information strategy Expert, John Weathington often sees many companies not determining their future big data strategy." As follows, he gave us the secret of how the enterprise successfully developed the Big Data development strategy.

Nothing is more than half a year after the company has developed a big data development strategy, and everyone in the business, even some of the top leaders, is not sure what a big data development strategy is, and how they should do something worse. Of course, companies can be bold enough to announce their big data development strategy, which will naturally excite everyone, but no one really understands the precise purpose of the business. As an information strategist, I always see all of this happening in different companies.

In general, my assessment of these situations is that six months later, only a handful of people in the enterprise really understand the purpose of the strategy. When the Big data strategy team sets out a clear vision, I look forward to seeing what directives the strategic team has been given by the top leadership of the enterprise. To be able to achieve the fastest productivity from your large data strategy team's efforts, you must make some assumptions about the framework strategy issues.

Competitive strategy

Before we make a framework issue for a large data strategy team, let's revisit the goals of the big Data strategy and the related components that make the strategy successful. We'll start by defining a big data definition as a competitive strategy: Big data is a lot of fast-moving and free-access data that may offer potential unique value to the market, but if used in traditional ways, prices can be very expensive.

From this definition, the Big Data strategy will create a blueprint for the future. Forecast the development of the enterprise in the next 3-5 years or longer from the strategic perspective. In order to achieve this vision, we need to identify the markets, products, services, and some relationships that must be developed, and most importantly, the central drive of the business leaders to ensure that decisions are made in these areas.

Finally, the development of large data strategies requires understanding of the key competencies of the enterprise, in turn supporting the implementation of large data strategies. The most notable is the large data development Strategy team is composed of data scientists, business analysts, process experts, enterprise leadership and management team.

Because the strategy of an enterprise (as opposed to a key project) is to focus on a long-term development plan, its implementation strategy will vary greatly. Implement a project, or even a short-term plan, focusing on work breakdown, related milestones and activities, integrating everything together. Risk is a matter of concern, but the situation of unexpected events is not always happening. A short project plan can help project managers achieve success. However, if you try to use this simple method to implement the enterprise's big data development strategy will certainly bring disaster. Risk issues are particularly important.

A one-year project would make it difficult to control risk, and the implementation of a three-year development strategy must take into account the priority of risk. Not just to make some short-term milestones, but also to take into account what happens during the period, you need to work out the appropriate coping strategies for several different situations.

This happens to be a major control turning point for the company. Of course, there are key decision points that make the business more influential. In this case, the data strategy expert must verify the relevant situation and identify the culture that is in fact a natural data science or other large data strategy team.

Framework Issues

When your large data strategy team's strategic assumptions are validated, they are most effective. I've heard a lot of data. Scientists are good at solving strategic problems. While this approach is much better than requiring data scientists to be involved in the overall development strategy from the outset, it is incumbent upon the strategic expert to give the baton to a large data strategy team before further steps are taken.

The best way to attract your data to scientists is to make some bold assertions that have important strategic implications and then ask them to prove your large data analysis assertions. In Data science terminology, this is referred to as a validation hypothesis, which is suitable for a scientific method that scientists have already known about all relevant data.

You may have encountered these scientific methods in high school or college; However, this method of data scientists is part of their culture. And since your data scientists are at the core of your business's large data strategy team, if you can familiarize yourself with this approach and ask your questions in this way, they will start working almost immediately with little guidance. Of course, this is a management process, so managers and leaders in your large data strategy team should be familiar with these scientific approaches. This is why it is quite important that it also tests the wisdom of your big data leaders and managers.

The scientific approach begins with a hypothetical question. For example, you might start a strategic question, "What type of customer is our product suitable for?" At this point, some analysts will post data to look for answers, but I suggest you build a hypothesis further.

You can assert it based on cooperation, research, or simply intuition: "Housewives value our products." "Of course, this sentence has strategic significance. This may involve readjusting your business's marketing efforts and possibly changing the product to meet the needs of the housewife more closely.

However, before you commit resources in this direction, it is important to verify this assertion of your large data policy team. They will test, using a complete analysis of scientific methods, and finally come to the conclusion. Even if they refute your assertions, their analysis is valuable and can help you start another hypothetical assertion.

A good starting point

If you are unfamiliar with all this, don't worry. Many companies are trying to get their big data resources in the best way possible. If you want a large data resource to develop an enterprise strategy, the risk stakes are very high. If you are completely at a loss and you are looking for a "Plug and Play" Management philosophy, you can consider data science expert Six Sigma (Six Sigma). Six Sigma can help you better understand process improvement, design within the framework of service delivery, build a scientific approach, and build high-quality solutions. Some minor modifications to the Big Data strategy will apply to your business strategy development.

Regardless of the regulatory framework, make sure you have to build your policy assertions to fully cover the risks, and then use your large data policy team to use scientific methods to validate these assertions.

(Responsible editor: The good of the Legacy)