4 effective ways to make more of your company's data
Data publikacji: 2015-07-26

4 effective ways to make more of your company's data

Peter Drucker, the most outstanding management theoretician, claimed that one of the major sources of issues for modern companies is focusing too much on external factors, such as costs and processes, while losing the sight of the basic task of every player on competitive markets, that is searching for new business opportunities and building a lasting competitive edge. How can your company cope with this?

Implementing modern analytical solutions will help to reverse this unfavorable trend and redirect the entire organization towards events that happen in its surroundings. However, in order to achieve it, new systems need to be introduced and, more importantly, the way data is used and the whole organizational culture must be changed.

Below are 4 recommendations that may help to take your company through the process.

1. Introduce a culture of making decisions based on facts

No manager will ever say it straight, but most of them admit that a significant part of business decisions made in companies are a result of internal games or the decision makers' intuition. Even the most agile data analysis system will not improve the quality and accuracy of decisions made on the basis of grounds other than those expressed in the form of figures and indices. Developing such a culture takes years. However, creating its foundations may already help to use Big Data solutions.

The key to success lies in identifying a manager or managers who can see the sense in a structured approach towards decisions and data. With time, they will attract other enthusiasts, who promote using data in the areas for which they are responsible. This is how almost a decade ago, through management succession, E&J Gallo, a global manufacturer and distributor or California wines, evolved from a family company – based mainly on direct relations and intuition – into a global leader in using data analysis for the performance of market strategy developed by the organization.

2. Cut down on hierarchy

One of the major barriers that hinder effective data exchange is the one related to strict hierarchy. In order to make the analyses and decisions taken on their basis more precise, you need to enable a relatively free access to data to a large group of recipients. However, implementing analytical tools does not mean that you have to revolutionize your structure. It is enough to operate a culture of openness and free thought exchange in teams responsible for data analysis.

3. Give your line managers access to analytical tools and training

Line managers are always the best source of information about customers and markets, as they coordinate the work of people in sales and customer service, along with business partners. Therefore, if you want to obtain up-to-date information on the company and its surroundings, you need to create a channel that will enable data exchange for people directly involved in their creation. This usually requires upgrading data analysis systems with visualization and automated reporting tools.

On the one hand, visualization in the form of clear and colorful graphs or charts helps to assimilate information, while on the other hand, it allows for noticing new correlations that would be difficult to spot in the rows of a spreadsheet. Automated report generation is a time saver for managers, even more so if the scope of data to be included in the report can be determined in just a few clicks, instead of writing scripts and commands. Time spent on designing a report with the use of a graphic generator is a few times shorter, and the risk of making a mistake is lower than when the users have to program all functions by themselves.

Automate, automate and... automate!

The biggest issues that prevent implementation of Big Data tools are: storing data in organizational silos (46%), lack of a clear business case for the implementation of an analytics platform (39%), ineffective management across teams (35%), storing data in legacy systems (31%); (source: Capgemini Big Data Survey, 2014).

This means that most of the issues stem from organizational and technological inefficiencies that hinder acquiring and using data across the company. These issues can be solved by automating at least some of the processes related to collecting, integrating and distributing data within the organization.

It is very important to assume that analytical systems should not make life difficult for their users, which means that they need to be as simple and intuitive as possible. Any complexities connected with data collection and processing should be hidden from those employees who use the systems to a limited extent. Otherwise, they may try to bypass the system, performing analyses by means of various other tools, according to their individual preferences. Unfortunately, by doing so, especially by using MS Excel, they limit the possibility of maintaining data integrity, comparing multiple analyses or integrating reports from various sources.

Introducing Big Data solutions to a company is always accompanied by some risk. The risk is even higher for organizations interested in using new tools, but less experienced in the field of data analysis. This is why companies should not hurry when deciding on introducing such platforms and build a solid business case to justify the implementation.One of the biggest mistakes is quickly starting the implementation without a clear statement of the company's objectives. There will be no ROI unless business objectives are defined, along with areas where using data analytics is most justified, and organizational structures and internal processes are adjusted accordingly.