Migrating to Dynamics365 is a notoriously long-running and complex project, taking organisations months, or even years. This article outlines the three stages of the project that absorb the most time, and shares tactics on how to drive efficiency at each step. So you can cut your migration time from months to weeks. Planning your Migration This is often the most time-intensive stage of most migration projects. It's difficult because organisations typically don't have documentation to map existing systems, processes, and information outputs. These assets are not typically readily available in most organisations, and creating them is a huge drain on project time.  Speed up this stage by: Applying a framework approach can dramatically speed up the process of documenting the existing data landscape, by utilising your business teams to support the work. We follow this framework to map the existing data landscape. Fixing Quality Issues Documenting

The data world is constantly evolving, and in recent years, data management has become increasingly complex. It requires a variety of skills, systems, and architectures to manage effectively.  This complexity has made data management less accessible to business professionals. The volumes of data and format that it is delivered in has made it much harder for business people to get useful information. It  has slowed down processes and limited the value of data for businesses. This article breaks down two approaches to address these challenges and simplify data management. So we can empower the business team to take ownership of the information in their organisation, and become more information-driven in 2023.  Prioritise Business Information The foundation of any information management strategy is understanding what data is available, and prioritising what is most important to the business model. In previous years, businesses have looked to systems

Despite investments in time, effort, and expertise, many organisations are still struggling to realise the value of their data.  Practical challenges like poor data quality, technical-debt and skills shortages grind data projects to a halt. Cultural challenges like a fear of data, a lack of trust and a lack of clarity on responsibilities reduce data adoption.  All of these factors make data uniquely difficult to manage. Most organisations haven’t yet found a successful approach, resulting in data remaining underutilised at best, and at worst, unmanaged. In the ‘information age’, this is often the biggest missed opportunity business are facing. Why is data difficult to manage? Today almost everyone’s job involves creating, using or interpreting data. Despite this, the majority of business teams believe they are unqualified to work with data, and are reluctant to participate in its management. It’s often unclear who is ultimately

Data-led projects are rarely delivered on time. Project Teams build in contingencies and utilise agile approaches, but continue to face delays. Resolving this issue is increasingly urgent as companies seek to become data-driven and convert data to business value. In this article, we’ll explore a powerful approach to reduce data project delays. We'll identify the different stakeholders in data projects, and how to utilise the data supply chain approach to manage these stakeholders.  Data project delays are caused by three common issues. First, is data availability. Data may exist within the business, but is not often readily accessible for data projects. It may be locked in a system, stored in a data warehouse, or depend on business teams for access. Extracting data technically, or building a process to source data manually from business teams, can be a labourious task.  Second, is data quality. Data

In the business world today we think of data as something that is really complicated, that you need to be highly qualified to understand. Consequently, companies that want to be data-driven are working hard on data training programmes to give their people the skills to work with data.  This wasn’t the case in the past. Historically, business teams naturally understood data. They were the people who created data and worked with it to generate insights. How did we become uncomfortable with data today? Well, it’s for two reasons:1. Systems and tech teams do things to data, and we don’t understand what they do.  2. There is so much data today it’s too overwhelming to understand it all. What mysterious stuff does the tech team do with data? Before we get into the role of the tech team, let's start with a note on technology. Technology,

If you work with data you may have heard of an information layer, but what is it and why are people talking about them? In this blog, we’ll explore the meaning of the term, as well as why it’s important and how it could help your organisation. What is an information Layer? An information layer is a repository of business-critical data that is automatically sourced, quality assured (with an audit trail), and combined into a shareable, ready-to-use, up-to-date table. This means that data only needs to be sourced, stewarded and engineered once, yet it can be used in multiple ways, whether it be a dashboard, an application, or a report.  Much like sugar refining, an information layer is taking something raw, (in this case data instead of a sugarbeet) and making it usable in many applications the way refined sugar can be

Many data leaders say that a data culture is critical in becoming a data-driven company. But what exactly does it mean, and why is it so important? We'll break it down for you below. What is data culture? Data culture is a mindset within an organisation. The objective of a data culture is to encourage the people in an organisation to utilise, understand and promote data in the everyday running of the business. Creating a data culture emphasises the importance of data in decision-making for all aspects of an organisation, in all departments. Why is data culture important? This year's survey of data leaders by NewVantage Partners identified cultural factors as 'the greatest barrier to organisations becoming data-driven'. While most companies have the necessary technology, creating the cultural change required to put data at the centre of decision-making is a much greater challenge. Encouraging

Digitisation, digitalisation and digital transformation may sound similar and are often used interchangeably, however, they have different meanings with important distinctions. These words are seen a lot, but what exactly do they mean? How are they different? And why does it matter? We’ve broken down the terms below. Digitisation Digitisation is the process of taking something analogue and making it digital. Many companies digitise when moving from a physical filing system to a digital one - for example, scanning customer information and storing it in a cloud rather than a paper system. Digitisation is a great way to preserve data and information and makes it easier to access and search. Digitalisation Digitalisation is a step further where you now take any data or process and use digital innovations to make them more useful to your company. Digitalisation is more than making information digital, it’s

We often hear data literacy mentioned as an important step in the data-driven process. Often used to refer to the education of ‘non-data’ employees, we know that data literacy is about upskilling groups of employees, but what does it mean in practice? And what is the impact of engaging in a data literacy program? What is data literacy? At its core, data literacy is removing the fear of data with knowledge and creating a mindset to embrace data innovation. A ‘data-literate’ employee is able to understand and use data with confidence in their daily role, communicate successfully about data concepts and collaborate on designing data products.  A workforce that is not ‘data literate’, may face challenges in data transformation due to a lack of engagement from business teams. This can impede collaboration on data projects, resulting in misinformed project briefs, slowed implementation and

The latest advice for companies striving to become data-driven is to ‘manage data as an asset’. But what does that actually mean? In this article, we’ll dive into what it means to have ‘data assets’, why you need them, and how to manage them effectively. What does it mean to manage ‘data as an asset’? Data creates value for a company. It’s utilised to inform business decisions, and draw insights that can translate into profit for an organisation.  Data can be as much of an asset as the people, inventory or technology that a company owns. However, many businesses still treat it as a by-product, leaving their data in an ‘unrefined’ state where it is of limited value.  To manage data as an asset is to adopt essential principles for that data that you would apply to any asset or resource, e.g. understanding the

There’s a lot of intel out there on how to get the most of your data. So much so, that some days it’s difficult to navigate the buzzwords and trends In this article, we’re cutting through the confusion to explain some core data concepts and explain how to use the ‘product’ method to connect data solutions to the business tasks that are completed, so you can increase company productivity and performance.  What are data products? A data product is a specific process or solution that uses data to address a specific need. Google flight alerts are a perfect example of a data product as they use data to notify consumers via email when flights of interest drop in price. It’s a great ‘data product’ because it’s completely aligned with the user’s needs, delivering precise information via a method that’s time-sensitive and on-demand. Is that

Data can be an intimidating subject, full of complexity and jargon. As a result, many business people we work with hold back on joining the conversation, as they’re worried they don’t have the expertise. In reality, data is just information. Business people generate this information in their day-to-day routines, and as a result, are actually in the best position to contribute value to the data conversation. Minimizing complexity in data discussions At Think Evolve Solve, our team has a blend of data analysts, engineers and scientists. But it also has business people from sales, marketing & executive roles. We help organisations build the most value into their data projects by engaging both business teams and technical teams in conversations about data. The simple questions below enable us to cut the complexity and be specific. Are you talking about data itself, or a data solution? Data is

2021 felt like a real step-change for us at Think Evolve Solve/gather360. The sheer amount of what our team has achieved is incredible, with our organisation celebrating several milestones and achievements in the past 12 months.   We’ve released several big features on our data supply chain management tool, gather360, that drive enormous efficiencies in how companies handle data management. We’re so excited about the results that we’ve produced, enabling our clients to deliver report-ready data 94% faster. We’ve applied gather360 to several new business challenges, with excellent results. Our data solutions marketplace now includes recipes to help with reporting challenges like SFDR, ESG Carbon Footprint Calculations, Audit Automation and Bordereaux Reporting, amongst others for the financial services sector.  In addition, the team has successfully deployed, maintained, and supported several complex data applications to our existing clients, and we’ve welcomed several new clients into our

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.  The 2021 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%)

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

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

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

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

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.