Top 7 Tips to Succeed with Big Data
Today all the businesses are focusing and investing on big data Analytics to offer reliable services and to get profits. Big data is playing vital role in making the better business decisions by enabling data scientists and other users to analyze vast volumes of data. Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. The analyzed information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue.
Companies are using technologies associated with big data analytics like NoSQL databases, Hadoop and MapReduce. These technologies form the core of an open source software framework that supports the processing of large data sets across clustered systems.
Here are the seven tips to succeed with Big Data.
1. Start small
In most organizations, big data projects get their start when an executive becomes convinced that the company is missing out on opportunities in data. Big data analytics can be done with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics and data mining. There are many unknowns when working with data that your organization has never used before — the streams of unstructured information from the web, for example. Which elements of the data hold value? What are the most important metrics the data can generate? What quality issues exist? As a result of these unknowns, the costs and time required to achieve success can be hard to estimate.
So it’s good to start small. First, define a few relatively simple analytics that won’t take much time or data to run.
2. Don’t try and change the business
The first thing is to stop trying to see technology as being a goal in itself and complaining when the business doesn’t recognize that your ‘magic’ technology is the most important thing in the world. So find out how the business works, look at how people actually work day to day and see how you can improve that.
Sounds simple? Well the good news is that it is, but it means you need to forget about technology until you know how the business works.
3. Get the scope right
It is known that 58 percent of respondents say inaccurate scope is responsible for their failed big data projects. Alex Rossino, principle research analyst at Deltek, says that the bigger and more unlimited the mission of an organization is, the more complex its data requirements will be – and consequently the more work it will take to get the scope right.
It’s better to talk to everyone who might be affected by the analysis, from the secretary or commissioner of an agency down to the heads of offices and programs. “It needs to be discussed and then kicked over to the CIO to determine the resources that are needed to make it happen and put in writing so that no one is expecting more out of a project than has previously been discussed.”
4. Finding a problem that needs a new solution
The next key thing is finding a problem that isn’t well served by your current environments. If you could solve a problem by just having a new report on an EDW then it really doesn’t prove anything to use new technologies to do that in a more time consuming way. The good news is there are probably loads of problems out there not well served by your current environments. From volume challenges around sensor data, click stream through to real-time analytics, predictive analytics through data discovery and ad-hoc information solutions there are lots of business problems.
Find that problem, find the person or group in the business that cares about having that problem solves and be clear about what the benefits of solving that problem are.
5. Making big from small data
Today companies very encouraged with all the big data tools that are being developed, released and leveraged. This reminds us that with the promise of big data comes the commitment to it being very accurate as well.
The unstructured data sources used for big data analytics may not fit in traditional data warehouses. Furthermore, traditional data warehouses may not be able to handle the processing demands posed by big data. As a result, a new class of big data technology has emerged and is being used in many big data analytics environments.
Big data is made of several smaller datasets and each dataset may provide niche value. By bringing all the datasets together, they offer big value.
6. Enable users for big insights
In the case of big data, success hinges on producing information that is of value. So it only makes sense to involve the people who will be using that information.
“Organizations with big data are over 70 percent more likely than other organizations to have BI projects that are driven primarily by the business community, not by the IT group,” says Aberdeen Group’s recently published “Go Big or Go Home? Maximizing the Value of Analytics and big data.”
“IT must recognize that big data means something different to every business and IT user,” says Evan Quinn, a senior principle analyst with research firm Enterprise Strategy Group (ESG). “The first question every agency needs to ask itself is, ‘What am I trying to get out of this. What is the value?’”
7. Defining Clear Business Outcomes
Instead of trying to integrate more and more data, it’s better to start with the business problem that needs to be solved. More often than not, IT works its way up to opportunity identification after integrating information from multiple systems. But investments to integrate all available data often lead to the accumulation of low-value analytics initiatives.
Innovative ideas are cool but also business impactful. In much the same way, innovative use cases for big data start with clear business outcomes, before they work their way down into the data and the analytic capabilities that are required to achieve these outcomes.