You have read reports of the promise of big data – how it has transformed so many businesses.
It’s all true. Big data analytics holds a lot of promises for generating tremendous value for organizations. And yet many organizations fail to realize notable results from their big data initiatives.
Why do they fail, while others succeed?
Because they make errors in the way they approach big data projects.
Here are three common mistakes that many organizations make. Avoiding these mistakes will make a significant difference to the success of your initiative.
1. Lack of a specific business objective
Big data holds huge possibilities. It truly does have the power to transform your business.
The trouble is, it’s easy to get carried away by the promise of what big data can offer. As a result, you might make inordinately high investments into your big data project.
On the other end of the spectrum, it can appear to be incredibly complex – something which your organization would not be able to invest in at this point of time. Consequently you might do nothing at all, waiting to be more prepared before you begin a big data project. But can you really afford to wait? What if your competitors begin to use big data successfully?
You might have significant volumes of data across several areas of your business. Ignore the appeal of using all of your data for the time being. Don’t start a big data initiative simply for the sake of getting into big data. Instead, do it for a specific objective.
Rather than think about big data in terms of a large investment, begin with a small project. Pick a single goal or a challenge and initiate a project around that. This should preferably be a business problem/goal that has a compelling benefit. Something that fits in with your organization’s overall strategy.
For instance you might want to figure out how to retain more of your existing customers. Or you might want to predict the demand for a specific product in the next quarter. These are specific challenges that you can begin with.
Defining a specific objective helps you focus and get started. However it has another benefit.
Starting with a single project makes it easy for various stakeholders across the organization to understand and appreciate the importance of your project. Consequently it becomes easier for you to get their support. When you have a clear goal, it becomes easier to mobilize resources for executing that goal.
2. Working in silos
Business problems are rarely about just one isolated area of the business. Therefore A big data project that is initiated by a particular department would need to gather data data from several sources outside that department. Sticking to the data that is available only within your department will only give you a limited perspective.
Here’s an example. For marketers to generate more leads and create more buzz, they need to be able to present the organization’s offerings to the right people at the right time. They have to create a positioning that genuinely resonates with a specific audience. There are many sources of data that can give you a better picture about what people want, but nothing is more powerful than your customer data. Yet, that data is not generated by marketing. It would originate from the customer service department.
Interactions with the customer service department can yield valuable insights about why someone became a customer, and what gaps need to be filled in the customer acquisition process. It might help the marketing team to identify a persona that will be more receptive to a particular product and thereby focus their efforts on that persona.
Similarly these insights can play a major role in helping the product team to create improvised products or add the right features to existing products.
Therefore take the necessary steps to ensure that the relevant data is shared across departments. That data might make a huge difference to your profits.
3. Not having the right team
Running a big data project needs a cross-functional team – a team with the correct combination of skill sets.
You need talented data scientists to run a successful big data project. However, you need people who understand the business side too. People who can work together with the data scientists to ask the right questions as well as draw the right conclusions from the data. You need a cross functional team which understands the three components of big data analytics – the technology, the mathematics and the business. Broadly, you need these three roles:
Data Scientist – Someone who understands statistics and can program statistical models in a language like R, Stata, Matlab, etc.
Developer – The person who builds and maintains the big data systems – Hadoop and the technology ecosystem.
Business analyst – The person who has an in-depth understanding of the business and can derive meaningful insights from the data.
Note – Some sources might refer to a ‘data scientist’ as someone who has all 3 of the above capabilities. Terminologies differ! Besides, practically speaking, its incredibly difficult to find someone who has all three skills.
Having the right people on your team is crucial for the success of your big data project.
Big data can make a huge difference to your business. Make sure you avoid these basic mistakes while initiating a big data project.