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A study conducted by EMC in 2013 shows that Polish managers do believe in the benefits of Big Data implementation; 36% of the respondents from Poland think that business decision-making process can be improved by more efficient data use. Furthermore, 39% of the respondents believe that only the companies and institutions that implement such solutions can be successful.
Despite being enormously versatile and universal, Big Data systems will not solve all issues related to information analysis. That is why, before deciding on a pilot deployment, you should think whether the new solutions will bring the expected value to your company as well as consider how the companies that work on the deployment can help to speed up the whole project.
Most importantly, such an analysis should answer to a fundamental question of the influence of new analytical possibilities on the market environment, the company's business model and the value offered to the customers. This means, in particular, analyzing the influence of Big Data on the business model of your company and those of its competitors as well as on the way in which they create value for end customers and on the estimated possibilities of overhead cost reduction thanks to the new trends in analytics.
State of the art analytical solutions open up new opportunities for every company. However, in order to benefit from them, first you need to deploy new systems properly. How is it done?Read more
Every manager who wants to implement some modern analytics solutions should ask himself/herself a few questions first.
Modern data analysis tools can be used effectively only in companies that operate a culture of decisions made on the basis of facts and free information exchange that is not limited by hierarchy. Contrary to the common belief, focus on data and facts is not so widespread, even in the world's largest companies.
According to the Guts and Gigabytes study conducted for PWC by Economist Intelligence Unit, as many as 30% of senior managers make decisions based on their own intuition and expertise, another 28% listen to other employees' advice and only 29% rely on data and their analysis. In companies where the intuitive model of decision making is used, implementing Big Data may turn out to be extremely difficult. Introducing new analytical tools in companies that do not provide suitable conditions to use the new knowledge causes frustration in employees and may eventually lead to a new crisis instead of optimizing functioning.
One of the greatest assets of Big Data is the ability to analyze various data related to behaviors and preferences of the recipients of goods and services. Using such tools makes sense mostly on mass markets or with products and services used in different ways by large and internally varied groups of users.
There is a whole range of customer service areas where Big Data features can be used. Marketing, as well as activities related to customer segmentation, preference evaluation, rating the customers' long-term value, or price and customer service optimization seem to the most natural ones.
Procter & Gamble is a particularly interesting example for the integration of modern analytical methods and data visualization (source: Thomas H. Davenport, Analytics 3.0). The company, which manages thousands of brands sold on hundreds of markets worldwide, has implemented analytics systems that enable quick comparisons of major indicators of goods movement (volume, number of orders, speed of stock replenishment), then share them on an IT platform available to the employees and display the data on dedicated screens across the organization. The purpose is to enable managers to request appropriate data visualization anytime and anywhere, so that on the basis of the information, they can make key decisions on deliveries and product range.
In the science of management, for at least two decades a trend has been observed to analyze more in depth the markets and divide them into segments. As a result, very detailed descriptions and divisions have been introduced that often have little to do with business reality. Therefore, before implementing solutions aimed at helping with the analysis, you should think of really how varied the business environment of your company is. The new opportunities of market segmentation by the sociological features and behavior models of customers are particularly eagerly used by banks to discover ambiguous patterns of actions by means of the new analytical tools, which enables them to prepare personalized offers for meticulously segregated groups of customers.
New methods of analysis are always connected with a certain degree of modification of the way a company functions. So before making an effort that can result in dramatic changes, consider whether your company can afford a transformation of its model and processes. There may be barriers (regulatory, cultural or financial) that do not allow for any material transformations and make ever more detailed analysis simply useless.
The key factors that determine the success of Big Data analyses are the amount and variability of data. The more data, the better the mechanisms that link various sources work and the more comprehensive results you can expect. The internet and modern social networks, geolocation data or the so-called opinion mining, that is an automated evaluation of the emotional attitude of the author of a message (a post on a forum, the way people talk when calling a hotline), are a never ending source of information that can be used for business purposes.
Of course, there is no need to use databases with millions of entries to make some basic assumptions. At the same time, the unique advantages of Big Data can be seen only when analyzing such extensive data sets. That is how global Citibank uses Big Data solutions (source:Thomas H. Davenport, Analytics 3.0).This institution is processing huge sets of data (financial, product, customer, economic data, financial reports submitted to the US Securities and Exchange Commission, prospectuses for securities, information published by press agencies and portals, and social media content) in real time to analyze the opportunity to offer new, secure services that meet the requirements of online banking. The use of Big Data is supposed to speed up the decision-making process, help to evaluate market opportunities as well as to assess any possible risks and consider alternative business scenarios in line with the bank and its customers.
The innovative potential of Big Data is so big that the question of implementing new analytics solutions becomes out of place. At the same time, the organizational costs resulting from a full deployment of such systems in a company are so high that the decision on implementation must be taken in the appropriate time. And before making the decision, the potential of the organization itself as well as its technological, management and material competencies need to be considered.