We strongly believe London is the data tech capital of the world. We’ve seen a lot of attention recently on the UK’s tech scene, with London, and Silicon Roundabout in Old Street, at the heart of it.
Innovative data tech start-ups have launched as a result, pioneering new approaches and bringing to market exciting technologies that help companies do amazing things with data, not previously possible.
Demand from London’s businesses to have access to inaccessible data faster than ever before without investing in major ‘big data’ projects is driving London’s data tech start-ups to think outside of the box.
Many businesses are struggling with analytics because they’re trying to do too much too soon. The hype around big data a couple of years ago caused companies to jump straight in before understanding what they really needed to meet their business goals. They invested in expensive on-site systems before clearly defining a strategy, pouring funds into failed data projects.
This has contributed to only 1 in 10 business leaders believing data is generating value for their company, according to research conducted by OnePoll, and sponsored by Rosslyn Analytics.
What we need is a much closer alignment, with IT and the rest of the business working hand-in-hand. It’s the only way that those making the make-or-break decisions will ever begin to tangibly see value from company data.
Businesses haven’t been put off of analytics projects because they know it has too much potential to create value and innovation. What has changed is how quickly the projects need to show ROI – the traditional “let’s wait a year or more for a return” view is no longer acceptable. Newer, business-friendly technologies delivered as-a-service mean a return can happen in weeks not months.
Previously, data analytics projects took months to implement, which not only drained resources and increased costs, but didn’t deliver expected returns due to poor user adaption.
This process has sped up dramatically because tools have become far easier to use and quicker to implement. Improvements in data migration and the emergence of cloud-based data platforms are just some of the driving forces behind this rapid adoption of analytics.
Although training and education is important, it is not about turning the world into a big group of developers or data scientists. Instead, it should be about simplifying the technology so more people can access the data they need, instead of it sitting with a few highly skilled individuals, who are often removed from the needs of the business. The so called “skill shortage” exists because of the lack of such business friendly data technologies.
As a result, all too often data sits in the hands of the IT department and the business decision makers will submit requests to access the information they need. It’s a ludicrously drawn-out approach. Not only does it increase the time it takes to put data in the hands of the decision maker, but it reduces the trust they place in it. Recent research carried out by Rosslyn Analytics found that just 16 per cent of business leaders had complete confidence that the data they were provided was correct.
Traditional legacy systems are not only expensive to maintain, but they require a set of highly skilled professionals to extract and analyse the data. And this pool of individuals is getting smaller – new, young grads coming into the job market have geared their studies towards newer, cutting-edge technologies, wrongly assuming this is what most organisations are now looking towards.
We are entering an era where organisations are realising the value of the data they sit on. The likes of Spotify and Tesco’s Clubcard have shown the world how data can be used to create competitive advantage. This has forced others to question why they are not exploiting the data they have available to them – and how this can be done without the huge investment required by traditional big data projects.
It’s forced innovation in data technologies, which weren’t previously up to scratch. And – as we’ve seen across the IT sector – this innovation has taken place in the cloud. The likes of Amazon Web Services have moved data into the cloud to reduce costs, and now the same is happening with analytics tools.
This is not only making data much more accessible to smaller businesses, but is putting data in the hands of more individuals because employees can simply log on to a platform, access the insight and make a decision.
It’s no surprise we saw a buzz around the launch of Azure given the size of Microsoft. What is interesting is that Microsoft has realised that there is a demand for a converged data architecture that incorporates data technologies and delivers it as a service.
Just using a visualisation tool is no longer enough; business users are looking for the control of obtaining, improving and managing the relevancy and quality of data required for effective decision-making. We expecting exciting innovations coming from Microsoft as they invest heavily in pushing the adoption of Azure in the market.
Rosslyn Analytics is a platform-based analytics company with a difference. Rather than providing a few data scientists with the tools to extract, analyse and derive insight from an enterprise’s data, Rosslyn Analytics’ cloud platform allows anyone in a business to embrace the data revolution, offering the ability to quickly collect, transform and manage information across all data sources and make it universally accessible across the business in minutes, not months.
It is the only analytics company to offer a complete, end to end analytics program on a single platform. RAPid – Rosslyn Analytics’ Big Data Cloud Analytics Platform – allows users to integrate, cleanse, enrich, analyse and visualise all on a single platform.
This not only reduces the time taken to see results, but makes it far easier to use in a collaboratively environment for business and IT users.
All businesses, regardless of size, need to carefully consider the overall strategy and what they want to achieve before moving forward.
This should then determine the technology, not the other way round. Only then can steps be taken – starting with simple projects before moving to more complex analysis of data.
Coverage from: ITProPortal