As football teams around the world enjoy their pre-season break, these can be exciting but anxious times. Especially for the teams and players making the step-up to play in a higher division after the success of promotion.
The potential rewards are substantial but making the leap to the higher echelons will be challenging, and preparation will be everything. Businesses migrating from traditional data management to big data implementations will be experiencing similar feelings – trepidation mixed with determination to make the most of the opportunity. This time spent in the run up to a new project, similarly with pre-season, will be equally important for businesses, as they plan methodically to ensure long-term success.
Not everything will be new of course. The basics of traditional and big data management approaches are similar. Both are essentially about migrating data from point A to point B. However, when businesses move to embrace big data, they often encounter new challenges.
With the summer transfer window now open, clubs and managers focus on bringing on board new talent and skills to ensure they give themselves the best opportunity for success. Businesses too will concentrate on ensuring they have the skills and tools in place. Over time, businesses will increasingly need to deliver data in real-time on demand, often to achieve a range of business goals from enhanced customer engagement to gaining greater insight into customer sentiment or tapping into incremental revenue streams.
It won’t always be straightforward. The volume of enterprise data is increasing exponentially. Estimates indicate it doubles every 18 months. The variety is growing too, with new data sources, many unstructured, coming on stream continuously. Finally, with the advance of social media and the Internet of Things, data is being distributed faster than ever and businesses need to respond in line with that increasing speed.
These trends are driving the compelling need for organizations to migrate to big data implementations. But as traditional approaches to data management increasingly struggle to manage in this new digital world, businesses look for new ways to avoid driving costs sky-high or taking too long to reach viable results.
The emergence of big data necessitates businesses moving to a completely new architecture based on new technologies from the MapReduce programming language to Apache Spark and Apache Storm Big Data real-time in-memory streaming capabilities to the latest high-powered analytics solutions.
There is much for businesses to do. From learning new technical languages and building new skills, to governance, funding and technology integration. Getting this right isn’t going to be an overnight success and businesses need to set realistic expectations and goals – just as in sport, managers whose teams are new to the top flight, need to take a pragmatic approach and not be too dispirited if they fail to match the top team at the first attempt.
This is where testing environments can play a key role too. At Talend, we’ve developed a free Big Data Sandbox to help get people started with big data – without the need for coding. In this ready-to-run virtual environment, users can experience going from zero to big data in under 10 minutes!
We have also identified five key stages to ensuring big data readiness:
- The exploratory phase
- The initial concept
- The project deployment
- Enterprise-wide adoption
- And finally, optimization.
Here are some key goals businesses will need to accomplish at each stage of their journey in order to ultimately achieve big data success:
In the initial exploratory phase, the focus should be on driving awareness of the opportunities across the business. Organizations therefore first need to become familiar with big data technology and the vendor landscape; second, find a suitable use case e.g. handling increasing data volumes and third, provide guidance to management on next steps.
The second phase is around the design and development of a proof of concept. The overarching aim should be IT cost reduction but the key landmark goals along the way will typically include building more experience in big data across the business, not least in order to better understand project complexity and risks; evaluating the impact of big data on the current information architecture and starting to track and quantify costs, schedules and functionality.
The next stage moves the project on from theory to practical reality. The project deployment phase specifically targets improved performance. Key goals include achieving greater business insight; establishing and measuring ROI and KPI metrics; and developing data governance policies and practices for big data.
Enterprise-wide adoption drives broader business transformation. It is here that businesses should look to ensure that business units and IT can respond faster to market conditions; that processes are measured and controlled and ultimately become repeatable. The final level of readiness is business optimization. To achieve this, organizations should look to use the insight they have gained to pursue new opportunities and/or to pivot the existing business.
My final recommendation is to make sure you build a clear and pragmatic execution plan, detailing what you want to achieve with big data success. Failure to do this may mean you don’t get the funding or support for a second project. It’s a bit like getting relegated at the end of your first season.
Fancy yourself as a data rock star? Find out how ready you are for big data success with our fun online quiz.