Data Management Success: Mapping the 7 Stages of Big Data Implementation

Big data is a major consideration - and if it's managed in the right way, an exciting opportunity - for businesses all over the world.

Research suggests:

• The big data analytics market will be worth $103 billion by 2023
• Internet users generate approximately 2.5 quintillion bytes of data every day
• 97% of organizations are investing in big data and AI
• Poor data quality is estimated to cost the US economy up to $3.1 trillion every year

There are many ways you can leverage big data and analytics in your company, from managing and optimizing your supply chain to studying customer behaviors and trends to better understand your audience.

However you're implementing big data, make sure you follow a clear, step-by-step strategy to mitigate risks and take full advantage of the opportunities that come your way.

1. Identify business needs and goals

At the very beginning of the implementation process, one element you need to be absolutely clear about is how this endeavor connects to the broader needs and objectives of the business.

Big data - and the various tools, functions and tasks associated with it - should be in service of the company's overall strategy, not the other way around. It’s therefore crucial to ensure you can draw a clear line between the time and resources you're investing in data implementation and business value.

2. Translate needs into data use cases

Once you've clearly defined the company's needs and priorities, you can start to conceptualize use cases that demonstrate how big data analytics can connect to these wider considerations.

If one of your most important goals as an organization is to derive greater value from your plant and machinery, for example, one possible use of big data could be the enablement of predictive maintenance for maximum cost and operational efficiency.

3. Understand data sources and systems

Another vital aspect of any big data implementation is getting to grips with the various information sources and systems available to you. This includes those you already use, as well as any new tools or processes you'll be adopting in the near future.

One of the topics you can take into account at this point is how data analytics and insights can be integrated with existing applications to maximize their value for the company. If you already have customer relationship management (CRM) software in place, for example, analytics solutions can help you derive new insights from the masses of data contained within.

4. Plan your big data architecture

Big data architecture refers to the various structures you have in place to dictate how large amounts of information are stored, processed, managed and made available to those who need to access it.

This will be made up of distinct layers dedicated to particular tasks and concepts such as:

• Your key data sources
• Management and storage
• Analytics
• Reporting

Your architecture could also include pipelines that funnel data to other repositories and applications where it can be put to use.

5. Analyze technical skills and requirements

Talent shortages are presenting significant challenges to businesses in various industries, but it's in specialist areas such as data analytics that skills gaps are most evident and potentially problematic.

Before embarking on any kind of big data implementation within your organization, you should have confidence that you can access the knowledge and capabilities required to deliver it to a high standard and achieve the outcomes you're seeking.

Make sure you take a proactive approach to this issue and give yourself enough time to identify potential skills gaps and put measures in place to address them. This could mean considering temporary recruitment to access hard-to-find capabilities on a short-term basis, or investing in reskilling or upskilling initiatives to develop talent internally.

6. Implement step-by-step

Once you feel sufficiently prepared and the time comes to execute your big data implementation, be realistic about how many tasks and processes your team is able to take on at any given time.

When you're dealing with complex data sets that are connected to a range of functions within your business, being overambitious in the changes you want to make could have negative consequences.

Rather than taking on the many responsibilities involved in such a demanding project all at once, consider using a phased approach that allows you to focus on key stages one at a time.

7. Test, measure and evolve

Another benefit of a step-by-step implementation strategy is that it gives you more opportunities to test, measure performance and make gradual changes to your processes as they become necessary.

Being able to adjust your methods in real time could prove particularly beneficial from an efficiency perspective, because it enables you to respond to challenges as they arise. If your approach to big data implementation is too broad or poorly planned, you'll run the risk of overlooking issues that could develop into serious problems.

With frequent testing, analysis and adjustment, you can keep your strategy on the right track and really make the most of your data.