McKinsey report: Let executives know big data analysis

In the past 30 years, many companies have added new management to cope with the vagaries of the business environment. In the middle of the 80, the CFO was a strange position for most companies. However, with the increasing transparency of value management and corporate-investor relations, more and more companies have CFOs. With the growing importance of brand building and customer management to the company, the chief marketing officer becomes more and more important, and many companies set up a chief strategist to help the company deal with the challenges from the market.

Now, the power of data analysis is profoundly affecting the business landscape. Seizing the opportunities brought by data development, increasing profits, enhancing productivity and even creating new business units, has become a new requirement for enterprises-this requires not only the talent and investment in information infrastructure, but also the reform of ideas, the organization of front-line training, and the improvement of the team's executive power. Without strong executive power, it will be particularly difficult to fully harness the huge wave of data analysis.

The impact of large data on the company is very broad, involving marketing, risk, operations, etc., senior management can participate in different ways. In some cases, tasks can be delegated to CIOs, chief marketing officers, and chief strategy officers. Other companies may need new roles, such as chief Data Officer, CTO, or lead analyst, to build a first-class team of data analysis centers.

This article is devoted to clarifying the most important tasks of the relevant executives and to the key issues, and the answers to these questions help to reset the structure of the executives. It may sound very difficult to rethink the roles and responsibilities of senior executives, but given the opportunities and challenges created by the current data development, the development of the enterprise will be confronted with a crisis and possibly into a more competitive environment without restructuring the executive structure.

The six major tasks of the data Analysis team

The development and implementation of a large data and advanced analysis strategy, not only to provide data to external service providers for data mining, but let the company in the day-to-day business of the way to respond to changes. The unpredictable environment puts strict demands on the executive team. Experienced executives are irreplaceable, they can apply theory to reality, guide companies through difficulties, make difficult trade-offs, and show authority when decisions diverge. The new data analysis culture will become the new focus of enterprise leadership, which is the inevitable trend of development. Experience has shown that the data analysis team needs to complete six tasks. When assigning tasks and adding new jobs, business leaders need to fully assess these six tasks.

Innovative thinking

With innovative thinking, the senior team of innovative ideas needs to acquire data analysis knowledge to understand the role of large data. They also need to accept the fact that data has become the core of the business. Only when the thinking and ideas of the company's top managers change, can the long-term behavior change radiate the whole company. In the early stages, a very important question is "how can data analysis help companies achieve leapfrog development?" This development usually takes place in every important business and functional department of an enterprise, led by an influential senior executive.

The leader of a large transportation company asked its chief strategist to be responsible for data analysis. To spread new ideas and knowledge among company executives, the chief strategist arranged for executives to visit large companies with data minds. He then asked each business unit to make data analysis one of the priorities for next year's strategic plan. The practice was very successful. On the one hand, large data is integrated into the strategic goals of each sector, and on the other, it encourages managers in various sectors to focus on large data. Soon they began to share their ideas and explore new opportunities for analysis-all of which gave the company new energy.

Develop data analysis strategies

As with other emerging business opportunities, the potential for data analysis has not been fully exploited because of a lack of clear strategies, plans and standards. Many companies have been frustrated in this area, in part because there is no clear executive responsibility for data analysis or planning, or because there is no adequate discussion or time to prioritize large data analysis.

The chief executive of a telecommunications company is committed to developing data analysis, especially the use of data analysis to optimize customer relationships and pricing. Although the company hired a senior analyst, it quickly stalled. To be sure, the analysis team has worked hard to delve into model and analysis techniques. However, colleagues in the business sector did not train the level of managers in time to use these models: they had not yet understood the potential of these analyses and models, as these were not their strategic priorities.

As mentioned earlier, a clear plan is needed to fully realize the potential of data analysis. Planning needs to focus and clear the path to achieve the expected business performance, which is similar to the process of strategic planning. Making such a plan requires the support of the team.

In a North American company, the chief executive asks the head of online digital operations, who has a wealth of data knowledge, to develop an enterprise development strategy. The chief executive also requested that the person in charge of developing a development strategy should work with other business units that are unfamiliar with large data. This collaboration-combining data with analytical technologists and experienced frontline operators-ensures that the analysis objectives outlined in the plan focus on practical, influential business decisions. In addition, this collaboration pattern has become a blueprint for other business units to plan their practices after the executives have shared each other's processes.

Decide on a construction project, a service that needs to be purchased or leased

Other important decisions also require authoritative, experienced senior leaders who are involved in data integration, build advanced analysis models and tools to improve operational conditions, and thus raise huge resource requirements. More and more external vendors are now able to provide core data, models and tools. Therefore, the enterprise needs the experience of the executive to weigh the "independent development or purchase service"? Is it necessary to develop these models and analysis tools in-house and to fully possess the intellectual property of these customized analysis technologies to meet the immediate development strategic needs and anticipated operational improvements? or scale expansion is vital, Is it wiser to borrow experience and manpower from external suppliers? Creating powerful data assets also requires the involvement of senior leaders. Restricting access to critical external data requires the establishment of high-level partnerships with customers, suppliers, or third parties in other value chains.

Different retailers have chosen a completely different path, which allows business leaders to understand the range of factors they must weigh. Some retailers and data analysis companies have signed long-term contracts that cover a wide range of data analysis requirements. Other parties, including traditional and online enterprises, have also invested deeply in the internal data and analysis technology. Each option reflects a dynamic set of strategic, financial and organizational requirements that should be determined by top management rather than by mid-level managers.

Ensure the technical expertise of data analysis

In any strategic plan, the enterprise always needs to analyze experts to help achieve rapid and stable development. The current era of data analysis game based on open, cloud infrastructure, so that all internal and external data can easily be integrated in a user-friendly manner. The new environment also requires new management skills to mobilize more senior data specialists. These experts can develop forecasting or optimization models to ensure the reliability of development.

At present, in the hottest market in the world, many companies are scrambling to find these advanced technology talents, gain the valuable manpower and let them interact with the business leaders, thus changing the development of the company is the real task of the senior managers in the future, which usually requires creative solutions.

The big data leader of a major consumer goods firm decided to invest in a data analysis center in a region far from the company's headquarters, which has a wealth of data scientists and data engineers in favor of talented people and cultural environment. Next, the company leader completes the final step by making direct contact between each analysis team and the business team of this part.

Invoke Resource

Calling people and capital between different functional departments, creating new decision support tools and helping front-line managers take advantage of advanced analysis models is often a surprise to business executives. Giving executives more power is important, which helps executives break down institutional limits, which often affect the support of data analysis for decision execution. To be successful, there is a need for managers in all departments to work together to respond positively to change-to encourage it, business units, analysis teams to collaborate, and to train experts for coordination and leadership. Companies that lack leadership often tend to fail.

For example, in a transport company, the role of middle managers in product areas is to look for data analysis opportunities and move on. The analysis team is frustrated by the inability of data teams to submit data on time or to submit data formats that are unsatisfactory. When you embed the analysis results in a custom tool, managers become more frustrated because they can only handle urgent requests in a regular budget and planning process. The company then asked a senior marketing director to optimize the data analysis process. The supervisor combines different functional teams, including database administrators, analysts, and programmers. They are dedicated to exploring data analysis opportunities and analyzing projects from inception to final landing cycles of 6-8 weeks. In just a few months after the top of the marketing executive, through agile Resource calls, the company managed to find several key points of analysis.

Create a first-line function

The complex data analysis solution designed by the data expert must be embedded in a first-line tool with a simple, strong interactive pattern, only so that managers and front-line workers are willing to use these tools on a daily basis. Efforts to promote the application of tools can not be underestimated, including formal training, on-the-job training. Experience has shown that many companies invest 90% of their investment in creating models, compared with 10% for first-line applications. In fact, the investment in front-line applications should be no less than 50%.

Play a leading role properly

Most companies will admit that they do need to add new jobs. But one of the big questions left is where to add new jobs? How should the new system of rights and responsibilities be designed? Experience has shown that companies have ample reason to emphasize data analysis strategies and talent, and even create regular data analysis centers. However, operational functions also require front-line activities (resource invocation, capacity-building) support. There are two reasons for this: first, there is a different focus on the use of data analysis to increase revenue and increase productivity, and secondly, it is equally important to actively encourage the front-line to make appropriate adjustments when the company combines the first line business with the core operations and management priorities.

In addition to allowing the business sector to strengthen its front-line mobilization capabilities and responsibilities, there is no single solution to clearly explain where the company should add new leadership positions. As the data analysis application is not mature, such an answer is not difficult to understand. Still, business leaders are not blind when looking at multiple options, and thinking about the answers to the three key questions will help business leaders straighten out the company's structural change plan:

Do you need to apply a core customer or operational database between different business units?

Is it necessary to create a large number of data analysis resources internally to retain talent and create proprietary assets and advantages?

At present, can managers of each business unit effectively respond to the challenges posed by changes in management patterns? Or does the company need a new executive position to specialize in data analysis?

When central data assets become critical

In many customer service companies, data analysis means consolidating transaction data for different business units or channels. This helps companies to understand the customer's interaction with the corporate web site, or the customer's decision-making mentality in choosing online shopping or offline shopping. These companies are often (or are) building new database centers or data environments, and upgrading related data management capabilities. In addition, they are developing new systems to protect customer privacy while ensuring secure data access and to ensure that core customers are not harassed anonymously.

For these companies, it is a more general and feasible plan to establish the chief information officer to lead the data analysis strategy and the talent building development. Responsibilities, CIO is committed to developing data analysis infrastructure and assisting business departments to adapt to change and seize the opportunity of data analysis.

For example, a diversified customer service company, with its board of directors and senior leadership teams, understands that using its own multi-channel database to hold data analysis opportunities will significantly improve business operations. Aware that the central database is critical to the corporate development agenda, the company leader assigns a CIO to take charge of and develop a corporate data analysis strategy.

Company management recognizes that each business unit has its own data analysis focus on the direction, such as optimizing the promotion of preferential prices or inventory status. In addition, different management teams need to apply different data analysis results to their respective departments. As a result, management concludes that it would not be advisable to have data centre management analysis and frontline training in these circumstances, and that the CIO should be able to work with the heads of the business units to take responsibility in a common and differentiated manner.

Currently, the CIO has been involved in two core projects. First, create new infrastructure to combine the company's multi-channel transaction data with external social media and competitive information, and push the data analysis result to the enterprise departments through the visual field; second, the formation of data analysis professional team, the different business departments to assign expert guidance, but the experts are unified management by the center. The data analysis team is led by an experienced executive who reports the process to the chief information officer. At the same time, business unit managers need to find their own data analysis focus on the direction of training front-line managers related skills.

When the internal data analysis ability becomes the key of enterprise operation

The second option. There are many similarities between this scheme and the first type of programme in centralized management, but the second one is specifically applicable to enterprises that decide to build their own data analysis platform without outsourcing. As a result, these companies typically focus on building data analysis facilities and teams in-house to create more value by creating a public platform of data analysis for each business unit in the company.

In a consumer-oriented company, data analysis and leadership are focused on the financial and risk management team. In the past, the team has long been responsible for key data-related value creation. When the company began to pursue a more ambitious data analysis strategy, the CFO was assigned several responsibilities, including the development of a basic strategy, a review of the core risk management data analysis tools, or outsourcing decisions, and a call to data Analysis team resources and data analysis capacity building.

However, after these initial decisions on data analysis, chief executives and CFOs soon realised that more support was needed to get more accurate results, to assist business units in adapting to changes in data analysis and to revolutionize certain processes in the business sector. To achieve their goals, they created a new position in the CFO's subordinate team, the chief data officer. The chief data officer is responsible for information management, and works with the Business department heads to explore potential, valuable internal and external data that may have never been discovered before. Many companies will find that they are very much in need of the head of the business unit, which can support the work of executives, to find more data advantages and to locate data analysis directions to speed up frontline applications.

When business unit size and complex data management become critical

Whether centralized or otherwise, the burden of data analysis falls on the heads of each business or functional department. The key issue for the business unit is whether to create new jobs or require key leaders, such as the chief Marketing officer or the operations director, to deploy new responsibilities within the load-saturated business units.

After a comprehensive understanding of the program by senior managers of a large financial services company, they believe that doubling investment in data analysis will significantly enhance the competitiveness of the business sector. The company has recruited a chief analyst to staunch the programme. The chief analyst reports to the line of business executives and directs and oversees data analysis centers consisting of internal consultants, analyst models, and software engineers.

This programme has greatly adjusted the structure of the company. It advances the business sector data transformation process. As a member of the executive team, the chief analyst can support a number of important decisions, including the development of a data analysis strategy, the definition of a front-line supervisor, and so on. Given that the analysis center is composed of people with interdisciplinary backgrounds, the chief analyst can flexibly invoke analysis and software programming resources to speed up the development of the first-line tools. At the same time, through the business sector, the chief analyst is able to have a deeper understanding of the specific situation of the business unit, including its focus, work model, and challenges. This helps tool development and training targeted. Daily communication between business executives and lead analysts enables them to focus more on data analysis and application processes.

After the success of such a programme, the company continued to move forward with the creation of another new post, the chief data officer. The chief data officer reports to the CIO but works with the chief analyst on a daily basis for data consolidation and development of new data analysis tools.

For companies seeking the potential for data analysis, they will need to choose where to add leadership positions in the near future. For some companies, such as the previously mentioned consumer-oriented company, the current executives have to assume more leadership responsibilities, so it is necessary to add new mid-level positions to support. For other companies, such as the financial services companies mentioned above, adding one or more new senior management positions to drive the data analysis plan may be the best solution.

All companies, executive teams, and perhaps board members, must recognize the scale of resources needed to support data analysis development. Next, they must carefully increase these functions at the current level of management, thereby effectively optimizing the company's core value sources and not causing the company's current structure to have a big impact. These tasks are daunting, but this is the only viable way to help companies use data analysis technology to promote their own development.

November 2013 Report of the McKinsey Global Institute, USA

Compiling: Center for international Economic and Technical cooperation, Ministry of Industry and Information technology Shering

(editor: Heritage)