Big Data: implementation from the technological perspective
Big Data project management requires some specific attributes like experience in projects that involve several areas of business; familiarity with software development projects; knowledge and experience with data collection, preparation, analysis and distribution; soft skills such as leadership, collaboration and creativity; wide view of the business, among others.
Some of the best practices related to Big Data projects are as follows:
- Be clear where data analysis will enable significant performance leaps: that is, working with masses of data from which significant value can really be extracted for the organization. Big Data projects tend to require a significant amount of time and effort. It is important to have an idea of the desired returns of this investment;
- Know what questions you want to answer: In order not to waste resources, ideally, professionals involved in Big Data projects should be clear about which questions will be answered with the analyzes to be developed. What business issues lie behind all the necessary efforts;
- Identifying what information is available: knowing what exists at the grass roots of organizations has now become a competitive differential. Leading companies in their industries invest time and effort in this direction. Before starting a Big Data project it is extremely important to know what you have “at home” and what is missing in terms of data and information;
- Prototyping: Like many projects involving business technologies, it is best to start with a prototype from which results will be measured, measured, and then replicated and expanded;
- Information culture: the most successful strategies related to Big Data occur in companies that have already implemented an information culture, in which people value information assets, seek to keep them updated, cataloged and effectively used in the various business processes and related decisions;
- Knowledge of available tools: As discussed in the first post of this series, there are a number of tools available that enable organizations to implement a Big Data strategy. In this sense, it is worth evaluating what will meet the objectives of each project in the most satisfactory way, since there are specific tools according to the objectives of analysis and the profile of the data that is available;
- Experience: As discussed above, it is important that Leaders involved in Big Data projects have seniority to deal with issues covering different business areas, diverse profiles of professionals, reporting results and gaining support from other leaders;
- Investments in usability: it is fundamental that the tools and interfaces developed in Big Data projects are adapted and oriented for the use by the end users. This is one of the factors that most contributes to the failure to achieve the goals of this type of project – users who perceive that the tools are complicated to use, impractical and functional. It is observed that in many cases little importance is given to training, provision of information in the interfaces, user manual, among other related aspects.
Companies are increasingly seeking to have speed to respond to the events of their business environments – customer preferences, competitors’ actions, legal rules, among others, are aspects that demand a high reaction capacity of organizations regardless of size or sector. Trends such as self-service, Internet of Things (IoT) and personalization of the consumer experience will generate huge volumes of data in real time. The ability to analyze information and respond to the environment will be one of the biggest competitive differentials that an organization can develop in the coming years. In some cases, it will be one of the key factors for your own survival.
In the next post, we will evaluate the challenges that organizations face in order to make feasible a Big Data project considering the human resources perspective required for this type of investment considering both the profiles of professionals related to technology and those who produce insights and analyzes from the data available.
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