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The Big Data Roadmap

Big Data is too important and has too many implications and advantages to be ignored

Knowing what Big Data is, is one; knowing what a Big Data strategy is two; knowing how to implement that Big Data strategy is even more difficult. At least, that is how a lot of organizations perceive it. It must be said, in large process-directed organizations, what most of the large corporates are, it can be difficult. Convincing the board and defining where to start could be a daunting task, when in fact the steps that need to be taken are clear and straightforward. This roadmap can help you define and implement the right Big Data strategy.

First of all, organizations need to understand what Big Data is to begin with, otherwise defining a strategy is impossible. Of course, I am here to help, helping organizations understand what Big Data is. In the near future, BigData-Startups will launch a Big Data Model that will help organizations understand all different variables that impact a Big Data strategy. This model will give organizations that want to develop a Big Data strategy the possibility to learn how all variables, and there are quite a lot of them, interact with each other and how they influence each other. It will give an automatic and tailored advice for your Big Data strategy. The advice includes details of an organization that has the Big Data technology whose services best fit with what the organization is looking for. You can leave your e-mail address here, if you want to be kept up-to-date when this model is launched or if your organization wants to be part of this innovative model. We will then notify you the moment the Big Data model is live or get in contact with you to make your organization part of the model.

Knowing what Big Data is can help you to get management buy-in at your organization. Big Data is a strategy matter, and not an IT matter. IT is merely supportive and therefore senior management or the board should be involved and support the decision to move forward with Big Data. Especially because in the beginning the returns on the investments made are still unclear and could potentially be negative in the beginning. Management buy-in ensures that the project is not stopped before any real results can be shown.

When senior management or the board approves the decision to move forward, it is important to get together a multi-disciplinary team from all different departments within the organization. Data tends to be kept in silos throughout the organization, if you focus on only one part of the company, valuable data sources could be kept left out. So, involve members from the marketing department to get the customer point of view. Involve product management to understand how data is gathered in the products or services offered. Involve human resources to understand the impact of data gathering on employees. Involve risk and compliance to ensure that your organization sticks to the four ethical guidelines discussed earlier. Ensure the finance department to keep the budget under control and of course involve the IT department to build the necessary hardware and software.

Involving all departments within the organization has a major advantage. When you start to define possible Big Data use cases, the brainstorming sessions will become a lot better when people from different disciplines are involved. Each member of the multi-disciplinary Big Data team can offer a different point of view on data and together a large pool of possible use cases can be defined. It is important during this phase to accept all possible use cases that are brought up during the brainstorming sessions (as in normal brainstorming sessions, ‘no' and ‘that's not possible' do not exist). It is essential to let creativity flow, as this will allow you to find new data sources previously not thought of.

Once you have outlined a few dozen possible use cases it's time to define criteria to rank all use cases. It will help to divide the use cases into different categories first, like use cases that fix bottlenecks or use cases that improve the efficiency of business processes. Use the criteria to rank all use cases in the different categories. Criteria can be the impact it has on IT, the impact to implement the suggested solution and/or a possible value proposition. It is no use to completely develop a scenario analysis for each use case, as there are too many unknowns at this moment.

Based on the criteria and the categories it is then time to select the Proof of Concepts that will be implemented. The multi-disciplinary Big Data team should be able to implement these Proof of Concepts with minimal effort. It is better to fail fast and fail often than to develop a complete solution and in the end notice that a wrong turn was taken. Although Big Data has the potential to bring a lot of positive results, it is likely that this is not visible from the beginning. Don't be afraid to fail and start over again in this phase, as it is part of the learning curve on how to deal with Big Data and to better understand how your organization can best benefit from it. For each organization after all, the benefits will differ.

The moment the first results come in, it's time to share the results immediately with the entire organization. Try to get the entire organization involved in the Big Data efforts, because for organizations to truly succeed with Big Data, an information-centric culture should be present. An information-centric culture ensures that all employees, wherever located, understand the impact of Big Data and will make information-driven decisions based on the available data. An information-centric culture will also ensure that privacy aspects are part of the DNA of the company, the privacy of the customers is respected and the organization sticks to the four ethical guidelines.

If the results of the Proof of Concepts are positive, it is time to expand the multi-disciplinary Big Data team throughout the organization and to start more and larger projects. As the organization has learned from the Proof of Concepts, it will be possible now to extrapolate the results of the first projects to the new projects. With the lessons learned it is feasible to better define a possible ROI, IT impact, possible process implications and other important criteria.

From there on, the entire process starts all over. For each new project that is done, it is of course important to fail less and implement faster, getting faster results and a more positive result. Whichever use case is chosen, in the end it will affect the net results of the organization positively, as long as the Big Data projects are implemented wisely and correctly. Of course, this is not always easy so in case your organization needs help, it is possible to get in touch.

In the end, this roadmap can help your organization develop and implement a Big Data strategy that is good for the company, good for your customers and good for society. Big Data is too important and has too many implications and advantages to be ignored. So don't waste time and get started developing your Big Data strategy.

This story was originally posted on BigData-Startups.com.

More Stories By Mark van Rijmenam

Mark van Rijmenam is a Big Data Strategist and the Founder of BigData-Startups.com.

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