“The world has become excited about Big Data and advanced analytics, not just because the data are big but also because the potential for impact is big.” – David Court: Director at McKinsey & Company.
Big Data Analytics is not just a project. It’s a journey, and there are steps you can take to improve your chances of success.
#1 – ADAPTING TO CHANGE
“How do you pick the framework that is here to stay? You don’t—because you can’t.” – Syncsort.
With rapidly evolving tools and frameworks, a challenge for businesses is to invest in applications that won’t need to be replaced in 12 months. Many are turning to Apache Hadoop for its speed and efficiency, but in an industry where change is the only constant, future-proofing Big Data Software has become a major investment for businesses.
“A recent Robert Half Management Resources Survey found that 41% of CFOs believe staying current with changing technology is the greatest pressure their accounting and finance teams face.” – Mark Sands: General Manager, Asia Pacific for BOARD International.
#2 – RESEARCH
“Part of efficient Big Data Analytics is selecting the right platform to help you through it. But what should you look for? And do you want to build your solution, buy it, or bridge an available software with what you have in-house?” – Sherry Tiao: Content Marketing Manager at Datameer.
This is where research comes into play, and what tools and technologies you decide to integrate rely fundamentally on what problem you’re trying to solve. One of the key factors to consider is what your data platform drivers are – storage or advanced analytics?
“For organisations needing to store and process tens of terabytes of data, using an open-source distributed file system is a mature choice due to its predictable scalability over clustered hardware. However, if you’re looking to run analytics in online or real-time applications, consider hybrid architectures containing distributed file systems combined with distributed database management systems.” – Nick Millman: Data & Analytics Leader for Accenture.
#3 – AD-HOC EXPERIMENTATION
“The earliest phase, where organisations experiment with and learn about their Big Data needs.” – Datameer.
This is is the initial step where the team is trying to understand what data can be analysed, who can analyse it, brainstorm ideas and identify challenges in a cost-effective and timely way.
“Typical problems encountered during the stage include missing or ill-prepared data, and the reliance on manual labor for data processing.” – Chris Raphael: Former Editorial Director & Content Strategist at RT Insights.
Experimentation is crucial for identifying problems early. It’s better to fail fast and fail cheap than to invest in the wrong platform and face disappointed customers.
“Fail often – obviously, try lots of things. As you discover what is working, do more of it. And what does not work gets cut and is not a failure, it is a learning of what does not work.” – Canrock Ventures.
#4 – THE RIGHT USE-CASE
“You need to know how, and why, Big Data is useful to your company.” – Talend.
What problem are you trying to solve, and what should you consider when looking for a Big Data solution? choosing the right use-case can be the difference between the success or failure of your Big Data project. Although it can be tempting to try and tackle the biggest and most complex business problem as soon as you’ve been given the go-ahead to implement a Big Data platform, the best approach is to start small.
“Go small. Very small. For example, starting with one low-key business problem and a few easily accessible datasets. If you don’t, you could unknowingly be winding down the path to failure.” – Ben Sharma: Co-Founder & CEO of Zaloni.
#5 – DATA GOVERNANCE
“At its core, data governance is about data trust and accountability, married with comprehensive data security best practices.” – Rob Marvin: Assistant Editor of PCMag.
A good data governance plan consists of a data management strategy, ongoing monitoring of data quality and selective access. “What’s the data you have, who has access to it, and how are you managing the lineage of that data over time?” Jack Norris: Senior VP of Data & Applications at MapR.
Data governance is not only used to manage risk, but also to make sure that there are as few errors as possible.“Through a proper process, companies can implement the appropriate data governance initiatives and framework, which creates structure and accountability to data.” – Desire Athow: Editor at TechRadar.
For more resources, please see below:
Big Data Projects
Adapting To Change
Big Data Use Cases