How To Get Started With Big Data Analytics
Big data is allowing companies to uncover big insights and it’s never been easier to get started. The cost of infrastructure and hardware has gone down while the potential benefits and capabilities have risen. Analytical platforms make data processing possible and modular solutions let firms start small and scale up. The big attraction is the possibility to understand customer data, to communicate on a more personal level and to be able to market products and services to the right people at the right time. Every organization should consider big data analytics to help them improve customer loyalty and gain a competitive advantage. The trick is knowing how to get started. We’ve pulled together some pointers to head you in the right direction and ensure that you can harness the full potential of your data:
Identify business use cases
It’s important to work out exactly why you are starting a big data project. It might be that you wish to understand your customer base better, to personalize your communications or to reduce your operational costs. Ultimately you will need to come up with a number of questions that when answered will help the business make better decisions. Insights could include things such as:
- Which customer segments perform the best
- Which customers have the highest lifetime value
- How much do we spend per marketing channel
To get to these answers it’s worthwhile running workshops across the business to let everyone exchange their ideas. You need to identify big data champions from both the business and IT sides of your company. After running workshops, more often than not some priority use cases will rise to the top, and you will have a good understanding of where best to focus your efforts. Your business use cases will be closely aligned with business outcomes, metrics and your long-term big data roadmap.
Build the right team
You will need the right team of people involved to ensure any big data project is a success. You either need to staff the project with the necessary big data skills or to use a strategic big data implementation partner. Team members should be experts in analytics and also have an in-depth business perspective so they can make decisions as to which solutions will be most effective both short and long term. If understanding how to approach big data seems a little daunting or internal expertise is being consumed with other data processing requirements, then external solutions may be the way forward. External consultancy organizations can help develop strategies and execute any initial implementation. Using external expertise may well help accelerate the integration of big data processing into your day to day business workflows.
Implement big data infrastructure and tools
You will need the right infrastructure, tools and architecture in place to support the implementation and ongoing use of big data analytics. As we touched on, the cost of hardware and software has decreased which means its possible to arm each person in your analytics team with the support they need and a huge amount of storage to boot. Of course, not all platforms are the same and the components required will depend on your needs, the use cases you’ve outlined and ultimately the data that you have available. It’s worth considering the following aspects before implementing any given solution:
- How will data be collected – ensure you have a clear understanding of the volume of historical data along with the nature and complexity of data flows. Data may be in CRM, financial or marketing automation systems. Once you know how much data you have and where it is coming from you will need to consider whether it needs to be sorted, cleaned or enhanced.
- How will data be stored – in short, the primary requirement is that your storage solution can handle enormous sizeable volumes of data and is scalable. It will also need to provide the necessary outputs to deliver data to your analytical tools. You will need to determine the choice of infrastructure such as relational database, NoSQL or Hadoop cluster. Alternatively, you can consider using cloud-based solutions, many of which offer subscription-based services, to take the pressure off of internal infrastructure.
- How will data be analyzed – your method of analysis will no doubt change depending on your use cases. You should consider whether analysis needs to be done in real-time or with a delay. Understanding whether you’re trying to discover, explain or predict will help you understand whether you’re looking for correlations, anomalies or trends.
- How will results be viewed – you need to enable users to easily visualize results, this can be done by using intuitive, drag and drop fields on a user-friendly interface. It is critical to provide self-service access for business users so that they can benefit from big data insights.
Run your data projects in sprints
By running your data projects in sprints you’re able to test, learn and adjust to ensure your approach is as effective as possible. Ideally, two to three-month experiments will allow you to see rapid results and constant implementations that will benefit your business. A great benefit of working in this way is that your infrastructure doesn’t have to be perfect from the outset. You can get started and learn as you go, making adjustments and scaling up solutions that work. Needless to say, every project or sprint you run needs to have tangible and measurable outcomes. That way you can build on small successes and reap the benefits of big data analytics from the start.
Consider data governance
It’s wise to consider data governance at the start of any big data analytics project. Organizations must be wary of storing redundant data. Instead, they should have a policy in place that outlines the lifecycle of data, where it comes from, what its needed for, where it’s stored and how long for. Teams within the business need to own their data and executives should support this. If you’ve started your plan with proper business use cases, then it should be clear as to the depth of data analysis that will be required to deliver the desired business outcomes. Implementing rules for data governance will ensure that all security requirements are adhered to and confidentiality is ensured.
With the right vision, approach and tools your company will be able to harness your data and gain powerful insights. Big data projects are becoming increasingly widespread in businesses and are being scaled as they continue to deliver. By outlining your business use cases, building the right team and implementing the right tools, you will have the opportunity to gain invaluable insights, make connections that were previously impossible and ultimately to drive your business forward.