Building a Big Data Strategy. Before we focus on big data strategy, let's look at what strategy means. Although it is associated with a military term, a dictionary search on strategy shows the meaning as a plan of action or policy designed to achieve a major or overall aim. This definition calls out the four major parts that need to be in any strategy. Namely, aim, policy, plan, and action. Now, we are talking about a big data strategy. So what do these four terms mean for us? When building our big data strategy, we look at what we have, what high level goals we want to achieve, what we need to do to get there, and what are the policies around data from the beginning to the end. A big data strategy starts with big objectives. Notice that I didn't say it starts with collecting data because in this activity we are really trying to identify what data is useful and why by focusing on what data to collect. Every organization or team is unique. Different projects have different objectives. Hence, it's important to first define what your team's goals are. Have you ever had the scenario where you see the temperature on the weather report and someone else highlights the humidity instead? To find problems relevant to solve and data related to it, it might be useful to start with your objectives. Once you define these objectives, or more generally speaking, questions to turn big data into advantage for your business, you can look at what you have and analyze the gaps and actions to get there. It is important to focus on both short term and long term objectives in this activity. These objectives should also be linked to big data analytics with business objectives. To make the best use of big data, each company needs to evaluate how data science or big data analytics would add value to their business objectives. Once you have established that analytics can help your business, you need to create a culture to embrace it. The first and foremost ingredient for a successful data science program is organizational buy-in. A big data strategy must have commitment and sponsorship from the company's leadership. Goals for using big data analytics should be developed with all stakeholders and clearly communicated to everyone in the organization. So that its value is understood and appreciated by all. The next step is to build your data science team. A diverse team with data scientists, information technologists, application developers, and business owners is necessary to be effective. As well as the mentality that everyone works together as partners with common goals. Remember, one for all. No one is a customer or service provider of another. Rather, everyone works together and delivers as a team. Since big data is a team game, and multi-disciplinary, a big part of a big data strategy is constant training of team members on new big data tools and analytics. As well as business practices and objectives. This becomes even more critical if your business depends on deep expertise on one or more subject areas with subject matter experts working on problems, utilizing big data. Such businesses might have subject matter experts who can be trained to add big data skills, and provide more value added support than a newcomer would have. Similarly, any project member would be trained to understand what the business objectives and products are, and how he or she can utilize big data to improve those objectives using his or her skills. Many organizations might benefit by having a small data science team whose main job is do data experiments and test new ideas before they get deployed at full scale. They might come up with a new idea themselves based on the analysis they perform. They take more research level role. However, their findings can drastically shape your business strategy almost on a daily basis. The impact of such teams becomes evident over time as other parts of your organization starts to see the results of their finding and analysis affecting their strategies. They become strategic partners of all verticals in your business. Once you see that something works, you can start collecting more data to see similar results at organizational scale. Since data is key to any big data initiative, it is essential that data across the organization is easily accessed and integrated. Data silos as you know, are like a death knell on effective analytics. So barriers to data access must be removed. Opening up the silos must be encouraged and supported from the organization's leaders in order to promote a data sharing mindset for the company. Another aspect of defining your big data strategy is defining the policies around big data. Although it has an amazing amount of potential for your business, using big data should also raise some concerns in long term planning for data. Although this is a very complex issue, here are some questions you should think of addressing around policy. What are the privacy concerns? Who should have access to, or control data? What is the lifetime of data, which is sometimes defined as volatility, anatomy of big data? How does data get curated and cleaned up? What ensures data quality in the long term? How do different parts of your organization communicate or interoperate using this data? Are there any legal and regulatory standards in place? Cultivating an analytics driven culture is crucial to the success of a big data strategy. The mindset that you want to establish is that analytics is an integral part of doing business, not a separate afterthought. Analytics activities must be tied to your business objectives, and you must be willing to use analytics in driving business decisions. Analytics and business together bring about exciting opportunities and growth to your big data strategy. Finally, one size does not fit all. Hence, big data technologies and analytics is growing rapidly as your business is an evolving entity. You have to iterate your strategy to take advantage of new advances and also make your business more dynamic in the face of change. As a summary, when building a big data strategy, it is important to integrate big data analytics with business objectives. Communicate goals and provide organizational buy-in for analytics projects. Build teams with diverse talents, and establish a teamwork mindset. Remove barriers to data access and integration. Finally, these activities need to be iterated to respond to new business goals and technological advances.