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How to build a big data ecosystem that delivers quantifiable value to businesses

29 Jun 2017

The world is becoming increasingly digitally dependent as people are becoming more and more comfortable with living their lives digitally.

And this means businesses have more opportunity to collect data, claims Rozetta Technology, a big data and analytics solutions provider.

According to global consultancy McKinsey & Co., retailers that leverage the full power of big data could increase their operating margins by as much as 60%.

A Gartner survey states that almost three quarters (73%) of organisations either had invested or planned to invest in big data in the period up to 2016.

However, Rozetta Technology stresses that despite such widespread investment in big data, only a fraction of the data collected is ever actually analysed.

David Sharp, CEO, Rozetta Technology, comments, “the point of big data isn’t necessarily that it’s big but that it delivers insights that allow businesses to operate more efficiently, deliver better, more useful products and services, and offer an individualised experience for customers.”

“In fact, using data more effectively lets companies seem smaller and more personal.”

Sharp explains that there isn’t one single big data solution, so companies must implement a range of technologies and methodologies to build a data architecture that’s secure, fosters collaboration and can easily scale as business demands grow.

“This is what’s known as the data ecosystem.”

Though, not just an alternative way of storing data, the data ecosystem’s role is to provide a single place where internal and external data can be organised, integrated, engineered, and modelled to:

  • improve process efficiencies
  • provide insights to increase understanding
  • lay the groundwork for transitioning data to information and, ultimately, to knowledge
  • present outcomes that improve decision-making for both the organisation and its customers

Sharp continues, “the data ecosystem includes infrastructure, analytics and applications, and all three are equally important to delivering value.”

“The infrastructure consists of technologies that form the ecosystem’s core and can store, process, and analyse data including unstructured data.”

“The analytics are the technologies such as business intelligence and machine learning that let businesses derive insights from data. Lastly, applications are the interfaces through which business users interact with data to gain insights that can drive decision making.”

Rozetta Technology suggests five key steps for businesses to consider when establishing a data ecosystem:

1. Strategic clarity

It’s essential to have a clear vision of what the data strategy aims to achieve. Businesses must define what success looks like, such as increasing customer value, increasing market penetration, or generating cost efficiencies.

During this step, big data project owners should look to secure executive buy-in. This will help ensure a strong data-driven culture pervades the organisation, giving projects legitimacy and value.

2. Data acquisition

It’s important to identify and integrate all internal and external data needed for the project.

Without a clear idea of what data is required to achieve the organisation’s objectives, projects can be derailed by gathering and analysing irrelevant or useless information.

3. Insights and knowledge

Once the data is gathered, data scientists or analysts can begin exploring and modelling the data to gain insights and knowledge. In this stage, modelling techniques can be tested against known problems.

Data analysts can use techniques like multiple regression, heuristic modelling, neural networks, or machine learning to establish how the data will be engineered to resolve problems.

This can be an exciting stage because it’s a time of discovery. It starts with resolving known problems and, through this process, other possibilities often emerge, giving organisations opportunities to gain further knowledge and insights they didn’t even know they needed.

Combining business knowledge and technical expertise, along with the information revealed by the data, results in a hothouse of idea generation. During this stage, the business can validate and confirm the strategy, and begin to identify and refine key areas for value creation.

4. Business process integration

Once insights and knowledge have been extracted from the data, the business can use it to design more effective work practices, efficient information flow, actionable reporting, improved transparency, and increased relevance of communications.

This is where the data starts to prove its value to the business as it goes from being theoretical to practical.

5. Management

Developing and implementing a data ecosystem isn’t a set-and-forget activity. It’s a journey. It requires an ongoing commitment from the organisation and a genuine desire to become data-driven.

A valuable data ecosystem empowers businesses to take a proactive approach to data, process, and customer engagement issues.

Moving forward, businesses must continue to work with the data ecosystem, updating and refreshing technologies, tools, and methodologies as appropriate, as well as reviewing key operation benchmarks and value expectations.

Sharp says, “when considering implementing a data-centric approach, organisations must start with an overarching view of the data ecosystem to ensure they choose the right combination of technologies and methodologies so that the investment yields the anticipated returns.”

“Many companies view big data as a magic bullet that will automatically help the organisation become more customer-focused and effective. This is only true if the business implements the right   strategy and solution for its unique needs.”

Sharp concludes, “working with an expert partner to extract maximum value from big data solutions is crucial, especially for businesses without the necessary resources required to effectively manage a data strategy from end to end.”

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