Executives understand Big data analysis

Summary: Invoking resources to invoke human and capital between functional departments, creating new decision support tools and helping line managers take advantage of advanced analysis models, often surprising to executives. Giving executives more power is very heavy

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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 an important legacy is where to add new jobs. How should the new rights and responsibilities system be designed? Experience has shown that companies have ample reason to emphasize data analysis strategies and talent, or 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:

1. Is there a need to apply a core customer or operational database between different business units?

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

3. Are the managers of each business unit able to respond effectively to the challenges posed by changes in the management model? or the company needs to create new executive positions 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 added new positions-the chief data Officer-to the CFO's subordinate team. 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-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.