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Think Evolve Solve is a fast-paced product startup taking an innovative approach to delivering trustworthy data. We work with well-known companies across multiple sectors to help them understand, discuss, and drive business insights with data. We are expanding our technology department to support our existing team of data scientists, engineers, and product specialists. What the role will involve: The Data Engineer is a critical role within our solution delivery process. Responsible for developing and maintaining end-to-end advanced data analytics solutions using gather360, our custom B2B data software, you will work alongside the leadership and development teams to deliver high-quality data insights to our customers across a range of industries. Day to day, you will: Employ of an array of technological languages (Microsoft SQL Server, Python) and tools (Excel, Power BI) to connect systems together and bring data to life.Design, construct, install, test, support and

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Data insights, gather360

Despite significant investments into data initiatives, many large organisations are not getting everything they want from the data in their organisation, and still have some way to go to meet their data objectives.  This year’s New Vantage Big Data and AI Executive Survey of 85 Fortune 100 executives found that just under a quarter (24%) of respondents say they have ‘created a data-driven organisation’. Despite 99% of respondents having made a significant investment in data initiatives such as Big Data and AI, only a third (30%) of respondents agree that they ‘have a well-articulated data strategy’.  The biggest struggle many firms face is not related to technology limitations but cultural barriers, with 92.2% of respondents identifying people, business processes, and culture as the biggest challenge to becoming data-driven. This is having a distinct effect on data outcomes, with just a third of respondents (29.2%)

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Data insights, gather360

Despite increasing investment, many companies are struggling to maintain momentum in their data strategies. A recent survey from TechCrunch states that 72% of large companies haven’t been able to create a data-driven culture.  With many organisations devoting more and more resources to their data strategy, this isn’t due to a lack of trying, however the sheer scale, complexity and number of employees at large organisations can often disrupt successful strategy implementation.  Here are some of the common challenges companies encounter on the journey to becoming a data-driven organisation.  Accessibility & Autonomy There’s a big difference between making technology available to employees and having them embrace it. One of the first challenges companies address is how to make data available in a secure way for staff to use, as demonstrated in a recent study cited by  Harvard Business Review that saw 77% of executives report

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gather360, Insurance

Insurance companies are undergoing the change from being ‘data rich’ to ‘data led’, embarking on digital transformation programs to increase the utilisation of analytics and automation. This article explains how to set up your enterprise for success with the latest approach to digital transformation. Covid-19 has been the catalyst for insurers to accelerate their digital agendas. The pandemic created new complexities around data sharing which requires secure cloud systems to be implemented. In addition, it has encouraged the industry to utilise new technologies. Insurers are adopting tools like intelligent automation, AI and ML to support the workforce as these technologies mature. Post-pandemic is the opportune time to develop these capabilities and drive efficiency in the workforce to enable new revenue streams. Becoming a data led organisation Insurance companies are traditionally 'data rich', transferring enormous amounts of information to power day to day

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Business, gather360

We’re proud to share that gather360 features in the RegTech Top 100 Buyers Guide for 2021!  The list identifies ‘the world’s most innovative tech solution providers'. This list focuses on providers that build 'solutions that address the challenges of ever-increasing regulatory pressures within financial services'. Think Evolve Solve is a leading solution provider in the cybersecurity & data security category. gather360 is designed and built by the Think Evolve Solve team. The platform manages the supply of data to reduce the time organisations spend on data preparation by 60%. This is the second consecutive year that gather360 has featured in the RegTech Top 100 list. You can read more about the platform here. It is also recognised as the world’s most innovative RegTech companies that every financial institution needs to know about in 2021. Previous   REGTECH100 lists have received worldwide attention and

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Business, Data insights, Large corporates, Use case

In the nine years since we started Think Evolve Solve, we've assisted enterprises across a variety of industries. Our team of engineers, mathematicians, developers and entrepreneurs has implemented a broad spectrum of solutions, from data strategy implementation to standing up advanced ML initiatives. No matter the solution, there has typically been one overarching reason why each organisation is looking to improve their data processes: digital transformation. The first step on our journey with a client is to share our unique perspective on how to put data at the core of this transformation. Most organisations looking at digital transformation start with the typical methodology of people, process, technology - by listing their assets in each area to understand how they need to transform. This method has one flaw - it's missing a view of the data assets. In today's world, an organisation's data is

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gather360, Use case

We've released a software! It's called gather360, and it offers a unique solution to a very familiar problem - ensuring good data quality. We started developing the tool after working on data quality solutions for clients in a range of industries over the past seven years. In each company we worked with, we saw data analysts doing repetitive and time-consuming work to clean and consolidate datasets they'd sourced from various systems, departments and external stakeholders. They were working with data they had already received, in the state that it had been sent to them, and doing much of the leg work to prepare it for use manually. We realised that if we sourced data differently, in a standardised, system managed way, we could drive huge efficiencies in this process and allow the data analysts to focus on data insights, not data preparation.