Source – analyticsinsight.net

The uninterrupted growth of Big Data in the world is putting forth a problem – that of managing this data. Therefore, organizations all over the world are looking for the “perfect strategy” to get up and running with their share of Big Data.

Implementing Big Data is a challenge for any organization, and for any strategy to succeed, an organization must be well aware of their needs and requirements. Without a clear understanding of these things, the laid roadmap might take you an altogether different destination.

Let’s take a look at five essential steps that you should keep in mind while laying out the roadmap:

1. Convene the Perfect Multidisciplinary Team

 Before even thinking of laying a roadmap, it’s essential to realize that Big Data is not an information technology project, it is a business initiative. So, the team you’re deploying for the same must have people from business and operations departments as well as the IT experts. Ideally, there should be more people from the former as they’re the ones who have a clearer idea of the business requirements.

2. Define the Scope of a Given Problem

While making sense of your data, be extremely clear about what problems you’re aiming to solve. Pick three issues you’d want to be tackled first, and formulate them into questions. Answering those questions will give you an idea of how you want to proceed with your Big Data. These answers will also guide your efforts in narrowing (or expanding) the initial scope of research. Such an iterative approach not only gives clearer insights but also allows you to go back and forth and fix any errors that might have crept in.

3. Assess Internal Data Sources and Silos and Gather External Data

Now that you have your team and questions ready, it’s time to let the cat out of the bag. Any organization has an internal inventory of data sources which will come in handy. While formulating a strategy, a team will want to have references, such as Vendor Contracts, Customer List, Prospect List, Vehicle Inventory, AR/AP/GL, Locations, and other terms that describe the purpose or system from which the data is derived. The list can be expanded for technologists later. More often than not, such information is stored internally in Data Silos.

Other than the internal sources, there are external data sources like Data.gov or your social media channels that generate a lot of data. Data.gov has more than 100,000 datasets, containing millions of rows covering decades. Download only five datasets for each of the three questions that you are trying to answer. For example, the Consumer Price Index (CPI) – Average Price Data from the Department of Labor Statistics includes monthly data on fluctuations in the prices paid by urban consumers for a representative basket of goods and services.

LinkedIn, Twitter, Quora, Facebook, Pinterest, and other social media channels have a more significant impact on the operations of your organization than you realize. Make sure to deploy a couple of team members solely to manage and study the data from social media.

4. Determine Output and Further Measures

Keeping in mind the questions you posed to limit the initial scope, determine what output are you expecting. You also need to understand who you’re pitching the end product to. Will they view it only on a large monitor, or might they see it on smaller screens too? Which data visualizations to use to display the output most concisely? How should the output be validated? There are many important points to address which will make the output understandable to everyone on the team – both tech and non-tech alike.

5. Be Holistic and Agile

Look at your output and analysis from all the dimensions. If your output makes sense, but you aren’t able to explain it to people around you, it’s of no use. Always look out for possible improvements in your final system. One of the four characteristics of Big Data is Veracity, and it talks about the anomalies and noises in your data. What this simply means is that there are chances you might come across errors you hadn’t thought of initially. That is why an iterative, agile approach goes a long way while implementing Big Data systems. In such cases, you need to readjust your budget, team, goals, and ideologies based on the circumstances you’re in.