Unravelling the Persistence: Why Are Legacy Systems Still Used? 

Unravelling the Persistence: Why Are Legacy Systems Still Used? 

Unravelling the Persistence: Why Are Legacy Systems Still Used? 

Legacy systems have long been the backbone of many businesses, providing reliability and specialised functionality. Despite the reliable role legacy systems play within these organisations, the rapid pace of technological evolution prompts us to scrutinise and ask the question: Why are Legacy Systems Still Used?  

The persistence in using these systems should no longer be happening and there is a necessity for change.  

The allure of the latest trends often overshadows the benefits of legacy systems. While the adoption of cutting-edge technologies may sound enticing in marketing materials, the reality is that many businesses continue to rely on systems that have been in place for decades.  

This blog post will delve into the reasons behind the continued use of legacy systems, the associated challenges, and strategies for navigating this complex terrain.  

Understanding Legacy Systems: A Foundation and a Challenge

Legacy systems, encompassing outdated technology, software or hardware, have become synonymous with the operational history of organisations. Although these systems are integral, their lack of modern features poses challenges, making it imperative for businesses to assess the need for change. 

Challenges of Legacy Systems:  

  • Maintenance and Support Challenges: As legacy systems age, vendors may cease support, charge premiums for maintenance, and withhold updates, leaving businesses vulnerable to operational disruptions. 
  • Security Vulnerabilities: Built with outdated technology, legacy systems become susceptible to cyber-attacks. The absence of regular updates exacerbates security risks, potentially leading to data breaches. 
  • Inefficiency in Modern Business: Legacy systems may struggle to meet the dynamic needs of modern business operations, hindering efficiency, scalability, and the ability to handle increased demands. 
  • Compliance Challenges: Evolving regulations may render legacy systems non-compliant, exposing businesses to legal and financial risks. Generating required reports for compliance becomes challenging, undermining regulatory adherence. 

If this list wasn’t long enough, we have discussed more problems with legacy systems here: Unpacking the Legacy: A Deep Dive into Investment Management Systems

Why Business Still Use Legacy Systems: The Dilemma

Despite these challenges, several compelling reasons drive businesses to maintain their allegiance to legacy systems.  

  • Cost of Transitioning: The upfront costs of transitioning from legacy to modern systems, including installation, integration, and employee training, pose financial challenges, especially for organizations on a budget. 
  • Fulfilling Critical Business Needs: Legacy systems, having evolved to meet specific business requirements, remain critical to operations. Replacing them risks disrupting business processes that have been finely tuned over time. 
  • Ease of Maintenance: Internal teams’ specialized skills and established relationships with third-party vendors offering support for legacy systems make maintenance easier than transitioning to unfamiliar modern systems. 
  • Customization and Specific Functionality: Legacy systems may have unique features tailored to specific business needs, making it difficult for organizations to transition without sacrificing critical functionality. 
  • Interoperability: Integrated with various systems, databases, and applications, legacy systems are customized to work seamlessly within existing ecosystems. Replacing them requires significant investments in time, effort, and resources to ensure compatibility.

Strategies for Navigating Legacy Systems: A Balanced Approach

While the allure of modernisation is undeniable, businesses must adopt strategies that align with their unique needs and constraints.  

  • Evaluation of Risks and Benefits: A thorough assessment of the risks and benefits of both legacy and modern systems is essential. This evaluation, done in consultation with internal teams, provides insights into which system aligns best with business needs. 
  • Modernization through Updating or Replacement: Incremental modernization by updating or replacing outdated components can improve functionality, security, and scalability. A phased approach minimizes disruption to business operations. 
  • Hybrid Solutions: Leveraging a combination of legacy and modern technologies through hybrid solutions allows businesses to enjoy the benefits of both systems, striking a balance between familiarity and innovation. 

The Future of Legacy Systems: Adapting for Continued Relevance

As technology advances, legacy systems will play a pivotal role in bridging the old and the new technologies.  

  • Data Integration: Legacy systems can integrate data with newer systems, providing a comprehensive view of business operations. 
  • Application Programming Interfaces (APIs): APIs connect legacy systems with newer technologies, enabling data exchange and leveraging the latest advancements. 
  • Web Services: Exposing legacy systems as web services extends their compatibility with newer technologies, prolonging their lifespan. 
  • System Orchestration: Utilizing legacy systems as central hubs for coordinating data and workflows between different systems ensures continued relevance. 


Embracing Modernisation: How to Better Your Business

While legacy systems have served as reliable workhorses for many businesses, the imperative to modernise is more crucial than ever. Here are why businesses should consider modernisation, even when fully entrenched in the familiarity of legacy systems.  

  • Enabling Innovation and Future-Proofing: Modernization opens doors to innovation, empowering businesses to harness emerging technologies like AI and data analytics for sustained success. 
  • Meeting Evolving Customer Expectations: Adapting to dynamic customer needs is crucial. Modernization ensures your organization can deliver seamless, personalized experiences, enhancing customer satisfaction and loyalty. 
  • Staying Competitive in the Digital Landscape: In a rapidly evolving business landscape, modernization is key to staying competitive. It signals a commitment to progress, positioning your business as a forward-thinking industry leader. 
  • Mitigating Security and Compliance Risks: Modern systems provide robust security features, reducing the risk of data breaches. Compliance with evolving regulations becomes more manageable, minimizing legal and financial risks. 
  • Enhancing Operational Efficiency: Legacy systems may become bottlenecks over time. Modernization streamlines processes improves productivity, and enables seamless scalability for business growth. 
  • Facilitating Interconnected Ecosystems: In today’s interconnected business world, modernization fosters collaboration by overcoming the integration challenges posed by legacy systems. 
  • Attracting and Retaining Top Talent: Modernization appeals to the modern workforce, attracting tech-savvy professionals and contributing to overall employee satisfaction and retention. 

In essence, while legacy systems have played a crucial role, modernisation is the strategic step forward for businesses aiming to thrive in the digital age. By embracing change, organisations position themselves for innovation, growth, and sustainability.

In conclusion, while legacy systems continue to be indispensable for many businesses, an understanding of their challenges and the adoption of thoughtful modernisation strategies are crucial. By navigating the delicate balance between the comfort of the familiar and the need for progress, businesses can embrace sustainable growth in today’s fast-paced business environment. 

Unlock the Future: Download Our Whitepaper

As you navigate the complex landscape of legacy systems and modernisation strategies, we urge you to delve deeper into the insights and practical guidance we’ve compiled in our exclusive whitepaper, titled ‘From Legacy to Leading Edge.’  

This comprehensive resource is crafted to empower CIOs, CDOs, and CTOs in the UK investment management industry with the knowledge and strategies needed to transition seamlessly into the future. 

Download our FREE Whitepaper, just fill out the form below!

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Setting the Standard: Engaging Data Achieves ISO 9001 Certification for Exceptional Quality and Customer Satisfaction 

Setting the Standard: Engaging Data Achieves ISO 9001 Certification for Exceptional Quality and Customer Satisfaction 

Setting the Standard:

Engaging Data Achieves ISO 9001 Certification for Exceptional Quality and Customer Satisfaction 

Everyone wants exceptional quality and customer satisfaction in their organisation – obviously! Companies are constantly seeking ways to stand out and deliver unparalleled value to their customers.  

This is why at Engaging Data, we embarked on a journey towards achieving ISO 9001 certification. 

This is a testament to our unwavering commitment to quality and customer-centric practices.  

This blog post explores the significance of our ISO 9001 certification and how it reinforces our dedication to setting the standard for excellence (and we want to boast a little bit, it is a great achievement!) 

Understanding ISO 9001 Certification 

ISO 9001 certification is more than just a stamp of approval – it’s a mark of distinction in the business world.  

This internationally recognized standard signifies a company’s adherence to a rigorous quality management system that ensures consistent excellence in products and services.  

ISO 9001’s emphasis on systematic processes, continuous improvement, and customer satisfaction aligns perfectly with Engaging Data’s core values. 

Engaging Data’s Commitment to Quality 

From the beginning, we have set a high bar in terms of quality.  

Our dedication to delivering products and services that exceed customer expectations has been the cornerstone of their success.  

Before pursuing ISO 9001 certification, we had robust quality management practices in place, making the certification journey a logical step forward. 

The Certification Journey

The path to ISO 9001 certification is no small feat, and our small back-office team embraced the challenge wholeheartedly.  

The journey consisted of multiple essential steps: 

  • Internal Assessment – Engaging Data conducted a thorough evaluation of their existing processes to identify strengths and areas requiring improvement. 
  • Gap Identification – Gaps between current practices and ISO 9001 requirements were pinpointed, providing a roadmap for enhancements. 
  • Implementation of Changes – Necessary changes were introduced to align with ISO 9001 standards, promoting efficiency and quality. 
  • Documentation – Processes and procedures were meticulously documented to ensure clarity and consistency. 
  • Employee Training – A culture of quality was cultivated through employee training and awareness programs. 
  • Pre-Certification Audits – Rigorous audits were conducted to ensure adherence to ISO 9001 criteria.  

Throughout this journey, our back-office team faced challenges that tested their resolve, but the spirit of teamwork and collaboration prevailed, driving them toward their goal. 

Benefits of ISO 9001 Certification

The ISO 9001 certification has already begun to bear fruit for Engaging Data: 

  • Streamlined Processes – ISO 9001 has facilitated the optimization of internal processes, resulting in increased efficiency and reduced waste. 
  • Enhanced Quality Control – Rigorous quality checks at every stage have become a norm, leading to higher quality products and services. 
  • Increased Customer Satisfaction – Engaging Data’s customer-centric approach is further fortified by ISO 9001, fostering lasting customer relationships. 
  • Improved Internal Communication – Clear documentation and defined processes have improved communication within the organization. 

Customer-Centric Approach

Our commitment to our clients shines even brighter with ISO 9001 certification.  

The systematic approach of ISO 9001 aligns seamlessly with Engaging Data’s client-focused values. Through consistent communication channels, we collect and act upon customer feedback, resulting in a continuous improvement loop that enhances customer satisfaction.

The Future with ISO 9001 Certification 

This certification marks not the end, but the beginning of a journey of continual improvement for Engaging Data.  

We are poised to maintain the high standards set by ISO 9001 while constantly seeking ways to enhance its quality management systems. Additionally, the certification could pave the way for further accolades and recognition within the industry. 

In achieving ISO 9001 certification, this demonstrates our commitment to delivering nothing short of excellence.  

This milestone is not just an achievement for the back-office team but Engaging Data as a whole! 

Yes, this blog post was us boasting a little bit, please forgive us!  

But it is exciting news for us and will benefit our future clients, giving them an understanding of our standards of working and always striving for greatness in both quality and client experience.  

How Data Automation Will Optimize Your Organisation

How Data Automation Will Optimize Your Organisation

How Data Automation Will Optimize Your Organisation


In today’s world it is arguably that the backbone of every organisation is Data.  

 From small startups to large corporations, data is everywhere and should be used effectively. For making informed business decisions, understanding customer behaviour, and improving overall efficiency – data is essential to drive further success.  

Yet, manually collecting, analysing and interpreting data can be a time-consuming and error-prone process due to the flawed nature of the human intervention. This is where Data Automation can be hugely beneficial to you and your whole organisation.  

Data Automation is the process of collecting, processing and presenting data using automated tools, instead of performing these tasks manually. Data Automation eliminates the reliance on manual labour with bots that do the job for you: more efficiently.  

With almost no human intervention, the automated process of collecting, transforming, storing and analysing data using well-designed methods, software and Artificial Intelligence will optimize your organisation.    





Data Automation can…

Save Time and Money:

With a plethora of automation tools on the market and being more accessible than ever before – why are you yet to adopt the technology that saves you time and money.  

Data Automation is designed with what people want in mind, making their lives simpler, eliminating the need to do manual, mundane tasks, and instead focusing on tasks where their skillsets are utilised effectively and proactively.  

It is easy to locate opportunities and areas for Data Automation on your own – once you know what to look for. A task that can allow for data automation usually involve:  

  • A lot of data entry 
  • Repeatable and repetitious actions 
  • Any margin for error 

Stop doing tasks manually and automate them. Optimize your team’s time with more meaningful work and reduce costs across your organisation.

Create Accurate and Fast Data:

The value of data comes from its quality. With mundane, time-consuming, and costly manual tasks completed by teams, it creates these processes to be slower and less accurate.  

With the adoption and implementation of Data Automation within your organisation, mundane tasks will become obsolete and replaced with automated processes.  

Data Automation can help analyse data faster and more effectively. With the ability to do a variety of tasks, Data Automation is especially helpful and can be used for data discovery, data preparation and data warehouse maintenance.  

Not only does Data Automation allow for your team to focus on more meaningful tasks which use their skillsets more effectively, but it bridges the gap and makes your data faster and more accurate – garnering more business success.  

Pairing Data Automation with Data Streaming and Data Quality tools will make your data faster and even more accurate, also allowing for: 

  • Durability 
  • Reliability 
  • Scalability 
  • ETL Capabilities 

Create Better Documentation:

Raise your hand if you enjoy data documentation? Let’s be real; documentation isn’t the most exciting part of working with data. However, its importance cannot be understated.  

The data documentation process can be difficult and not the most enjoyable. However, automating data documentation is an obvious solution to the problem that you face when working with data.  

Removing manual work of maintaining the documentation and creates a consistent process, overall ensuring reliable and trustworthy data and insights across your organisation.  

Documentation is one of those things you’d thank your past self for doing, it is always a great resource to look back on.  

Understandably, you’re probably too busy to document everything like decisions, statuses and steps for handling repetitive tasks. So, why don’t you automate it!  

Automating your documentation process will: 

  • With a single source of truth, save time and energy  
  • Improve quality and process control 
  • Cuts down duplicative work 
  • Makes hiring and onboarding simpler 
  • Make everyone in your organisation market with a single source of truth. 

Teams who are yet to start automating their data documentation are missing out on serious time, capacity and data literacy opportunities.  

Make all your Data in One Central Repository:

Imagine having one single place where you would have one single source of information. Sounds like a dream, right?  

Well, make that dream a reality with the implementation of a central Data Repository.  

A central data repository is a collection of stored data from existing databases merged into one so that it may be shared, analysed or updated throughout your entire organisation. It is essentially created by integrating the data from all available sources. 

Having all your data in a central repository allows for your data to be easily organised, analysed and secured. As well as this, it can help your business fast-track decision-making by offering a consolidated place to store data critical to your business operations.  

With ETL Data Automation tools, you can Extract, Transform and Load data seamlessly and efficiently into a central data repository, whether that is a Data Warehouse or a Data Lake, for example. 

Make your Data Storage System Scalable:

The need for a secure, reliable and efficient data storage solution has increased. Yet, businesses struggle with data storage as a result of the proper infrastructure to handle growing data. 

With the fluctuation and expansion of business, a scalable data storage system is a necessity to cope with needs and the quickly changing nature of business.  

Using Data Automation software, it is ready to scale as your business expands, as well as balancing your team’s workloads, highlighting bottlenecks and reducing resource consumption.  

Scalable data storage solutions are flexible, easy to manage and can handle exponential growth – far superior to outdated, traditional solutions with limited functionality.  

The data storage solution you choose should be reliable and efficient to allow your business to thrive.  

It can be difficult to choose with a plethora of storage solutions on the market, yet working with us, the Experts behind Data Automation, we will make the solution simple and adhere to your specific requirements. 

Modernise your Legacy Data Warehouse:

Organisations in today’s modern business world are being bombarded with data from various sources. Data which you need to collect, analyse, store and ultimately use in order to drive business decisions.  

Legacy Data Warehouses weren’t built with today’s digital capabilities and requirements.  

They are slow, rigid and generally expensive, with upfront and ongoing maintenance costs. This results in a more limited set of analytical capabilities, and it is slower to uncover business insights – making the decision-making process significantly slower. 

Modernising your Legacy Data Warehouse is a necessity for your organisation. Despite it not being the easiest process, you will benefit hugely from the modernisation of your Data Warehouse. 

Here some benefits of Modernising your Data Warehouse and working with the Experts behind Data Automation, to achieve this modernisation: 

  • Cost Reduction 
  • Improved Profitability 
  • Sales Projections  
  • Standardized Processes  
  • Improved Efficiencies  

With data growing significantly, your business will need an infrastructure that can manage and store this data to provide you with valuable insights and stay ahead within the competitive marketplace.  

Modern Data Warehouses are more flexible, intuitive, and efficient when it comes to storing and managing data. 

Increase Productivty within Your Organisation:

In search of optimisation and efficiency within business, Data Automation is the way forwards, and your company should embrace it.

Data Automation has always been propelled by the desire to get more done, reduce costs and limit human error, simultaneously.  

Create a higher level of efficiency with Data Automation. 

Eliminate Data Silos:

When it comes to decision-making, intuition is fine, but data is even better – you should rely on it. 

However, Data silos are a pain point for a lot of companies. Being a big blocker for decision-making, Data silos often get in the way of your business success.  

A data silo is a repository of data that’s controlled by one department or business unit and isolated from the rest of an organization. Often common in bigger companies, data silos can arise in any sized company and cause huge issues: 

  • Give an incomplete view of your business 
  • Create a less collaborative environment 
  • Lead to poor customer experience 
  • Slow the pace of your company’s growth and development 
  • Create security risks 
  • Threaten the quality and accuracy of your data 

With the implementation of Data Automation, Data silos will become obsolete, breaking down Data Silos and connecting data assets by: 

  • Data integration 
  • Data Storage 
  • Enterprise Data Management & Governance 
  • Culture Change surrounding data 

With all these benefits it is simple to say that implementing Data Automation within your organisation is a no brainer! It is a powerful tool to have in your organisation’s toolbelt. 

 It can optimize your organisation by streamlining data collection, improving accuracy, enhancing data analysis, increasing productivity and improving decision-making.  

By automating your data processes, you can save time, reduce errors and make better use of resources.  

At Engaging Data, we understand that you need data built efficiently to gain value quickly.  

Using innovative Data Automation tools, we will help you seamlessly integrate your data into accessible and secure platforms.  

Building Data for a purpose, we only process your relevant information to achieve your goals.  

Do more with less effort.  

If you haven’t already implemented Data Automation in your organisation, now is the time to consider doing so.


 Implement Data Automation within your organisation and work with The Experts behind Data Automation.

Start your Data Automation transformation.

Get in touch or fill out the form below to discuss how Data Automation will optimize your organisation:



How to Create and Manage a Data Science Team

How to Create and Manage a Data Science Team

How to Create and Manage a Data Science Team

Data Science is a relatively new, evolving and exciting data function. As this article explains, different organisations have various ways of organisation their data science teams, along with its managing them.

Organisations increasingly see data as a valuable asset that will help them succeed, now and in the future.

The value of data has been increasing in recent years, and it shows no sign of slowing down.

The first barrier to effective data and analytics is still the lack of qualified talent. Other familiar challenges include limited access to siloed data, lack of processing power and the absence of a data strategy to help turn data into actional information, which we have discussed previously here. More and more organisations are creating data science functions to lead their efforts in data mining, predictive modelling, machine learning and Artificial Intelligence (AI).

We have created this guide to provide best practices for creating and managing a Data Science team.

We have included the different ways a team can be set up, the positions it is likely to form and the executives to whom a team may report.


Models for Structuring a Data Science Team:

Data collection, management and analysis are typically the responsibility of the Chief Information Officer (CIO). The IT team works with business users to implement data warehouses and business intelligence (BI) systems that hold and organise data, enabling fundamental analysis and reporting.

However, over the past two decades, more organisations have separated the data function into their department as internal data stores grew, supporting technologies evolved, and data-related tasks became more differentiated and specialised.

The increasing importance of analytics to business success also drove the need for a data science team with skilled Data Scientists and Engineers. Today, many organisations, anything from a team or an entire data science department, provide this service. Larger organisations may have multiple teams that operate independently or in a coordinated way.

These teams are tasked with collecting and cleaning data from various sources, identifying patterns and insights, and presenting their findings to executives with actionable recommendations. Often this involved working with internal teams, external partners and vendors specialising in certain analysis types.

Data Scientists may work in areas like Sales and Marketing, Finance and Accounting, Product Development, Human Resources, Customer Service, Operation Management, Risk Management, Legal Affairs, Compliance/Governance, etc.

How a company structures its teams vary based on its Data Science program’s maturity, data analytics goals, overall organisational structure and enterprise culture. However, some common Data Science team structure models have emerged, each with pros and cons.

Team structure can be:

A Decentralised Team:

Where members work within the individual business units they support. This allows team members to collaborate closely with businesses on data science projects.

This approach can under the strategic use of data across an organisation and require more resources than smaller companies may have available.

A Centralised Team:

That consolidates a data science function into the enterprise, which manages individual projects and oversees resourcing. This approach allows for an enterprise-wide strategic view and uniform implementation of analytics best practices more efficiently.

However, it can limit the ability of team members to become experts in a particular area of the business. Some organisations establish a formal data science centre as a centralised team.

A Hybrid Team:

This approach creates a data science team who centrally manage all project with specific business operations. This team is accountable for helping those units reach their objectives and make data-driven decisions.

In hybrid structures, a centre of excellence may also focus on promoting best practices and standards for data science. As with the decentralised model, resource constraints can be an issue.


Data Science Team – Roles and Responsibilities:

Successful data science teams share common structures, roles and responsibilities regardless of the size or scale.

Small organisations with limited analytics needs or early-stage data science initiatives may have a generalist handle all the required tasks. Larger entities and those with more mature programs typically include some combination of the following roles in their data science teams:

Data Scientists – Data Scientists are key team members, using statistical methods and machine learning algorithms to analyse data and create predictive models. They also build data products, recommendation engines and other technologies for various use cases.

Data Scientists typically have multiple skills in mathematics, statistics, data wrangling, data mining, coding and predictive modelling. Expect people in this role to have advanced data science degrees or graduate-level data science certifications.

Data Analyst – Data Analysts are responsible for collecting and maintaining data from operational systems and databases. they use statistical methods and analytic tools to interpret the data and prepare dashboards and reports for business users. Data Analysts do not have the complete skillset of a Data Science, but they can support data science efforts.

Data Engineer – This role is responsible for building, testing and maintaining the data pipelines that power a business. A Data Engineer uses software engineering and computer science skills to focus on the technology infrastructure, data collection, management and storage. Data Engineers work closely with Data Scientists on data quality, preparation, model deployment and maintenance tasks.

Data Architect – Data Architects are responsible for designing and overseeing the system design and infrastructure implementation. A Data Engineer can also assume this role.

Machine Learning Engineer – Sometimes, this role is referred to as an AI Engineer: Machine Learning Engineers are responsible for creating, deploying and maintaining the algorithms and models needed for machine learning and AI initiatives.

In some organisations, Data Science teams may also include these positions:

Citizen Data Scientist – An informal role can involve business analysts, business-unit power users and other employees capable of doing their own data analytics work. Citizen Data Scientists are often interested in understanding or training in advanced analytics. However, their technologies – for example, automation machine learning tools – typically require little to no coding. They often work outside a data science team but may be incorporated into ones embedded in business units.

Business Analyst – Business Analysts are key in supporting the work of data scientists. Data Scientists are responsible for tethering, cleaning and organisation data, as well as creating new or altering models that predict what will happen in the future,

In addition, Business Analysts may be attached to Data Science teams, which includes evaluating business processes and translating business requirements into analysis plans, areas in which they can help support the work of data scientists.

Data Translator – The term ‘analytics translator’ is relatively new in the corporate world, but it refers to a very important role growing in popularity. The Analytics Translator acts as a liaison between Data Science teams and Business Operations, helping create and plan projects and translate the insights from data analytics into recommended business actions. This role often falls to the Business Analyst.

Data Visualisation Developer or Engineer – They’re tasked with creating data visualisations to make information more accessible and understandable for business professionals. However, Data Scientists and Analysts may handle this role in some teams.

Regardless of sector or industry, Data Science teams need to be strong in three core areas: Mathematical, Technology and Business Acumen,. It is rare to find a single person that excels in all three. Often companies will have someone fluent in two of the three, and then the rest of the team is built around that, filling in the caps to ensure the team is strong in all three.

Simon Meacher, Managing Director, EngagingData


Management and Oversight –

A Data Science team will be managed and overseen by either a lead Data Scientist, Data Science Manager, Director of Data Science or a similar managerial position.

The reporting structure for teams similarly varies. Generally, organisations assign a C-Level executive or high-ranking functional manager to oversee the Data Science team.

A Chief Data Officer (CDO) often oversees the Data Science function.

In 2002, Captial One created the first CDO position within the Financial Services industry. Since then, the CDO role has grown in popularity.

This role initially focused on Data Governance, Management and Security functions. More recently, CDOs have also taken on responsibility for Data Science, Analytics and AI.

Other organisations have created a Chief Analytics Officer (CAO) role to oversee their Data Science and Analytic teams.

Hybrid roles exist, combining the CDO and CAO responsibilities into a Chief Data and Analytics Officer role.

The head of a Data Science team may be subject to matrix reporting, allowing the role to report to a different Executive; for example, the COO, CFO or CIO or a position such as Director of Analytics, Business Intelligence Director, Head of Business Data or Director of Data and Strategy.


How Data Scientists work with Business Users –

Organisations within all industries are recognising the need to become data-driven and see it as a key to remaining competitive and set up the Data Science team to collaborate with business teams to:

– Understand the business problem or questions that the team want to answer

– Set and articulate the objectives for using the data.

– Plan how to apply the knowledge to make decisions and take action.

Once they understand Data Science teams cannot merely present their findings. They work with the business teams to understand the insights gained from the data and how that information can shape product and service offerings, marketing campaigns, supply chain management and other critical parts of business processes and operations to support company goals., such as: higher revenue, increased efficiency and better customer service.

In my expereince, Data Sceince teams need to work closely with the business. without using the wealth of knowledge about the data from the business, the Data Scientists will struggle to provude value from the data.

Carl Richards, Head of Consulting, EngagingData


Tools that a Data Science Team Needs –

Dozens of tools, ranging from data visualisation and reporting software to advanced analytics, machine learning and AI technologies, enable Data Science teams’ work. The number and combinations of technologies needed can be unique to each team based on its goals and skill levels.

The following is a list of commonly used Data science tools that include bothering commercial and open-source technologies:

– Statistical Analysis Tools: SAS and IBM SPSS

– Machine Learning frameworks and libraries: TensorFlow, Weka, Scikit-Learn, Keras and PyTorch

– Data Science platforms from various vendors that provide diverse sets of capabilities for analytics, automated machine learning and workflow management and collaboration programming languages: Python, R, Julia, SQL, Scala and Java

– Jupyter Notebook and other interactive notebook applications for sharing documents that contain code, equations, comments and related information

– Data Visualisation tools: Tableau, QlikView, Power Bi, D3.JS, Matplotlib

– Analytics Engines and Big Data Platforms: AWS, Azure, Google BigQuery, Hadoop, Snowflake, Spark

– Cloud Object Storage Services and NoSQL Databases

– The Kubernetes container orchestration service for deploying analytics and machine learning workloads in the cloud.


Best Practices for Managing a Data Science Team –

Executives and team leaders seeing to build and mature their Data Science programs should consider the following best practices for managing their teams.

Seek out workers with a range of business, interpersonal, and technical skills to help ensure that the team can meet organisational objectives.

Create a culture of learning and innovation that challenges team members and encourages them to bring new thinking to business problems and issues.

Promote analytics projects that encourage close collaboration between the Data Science team and the business units they support.

Evaluate team members at least partly on the business successes and work drives. Create a mentorship program to help advance the skills of junior team members, and do ongoing training to ensure that all workers stay current on key data techniques and technologies.

Talent retention programmes will help keep Data Scientists, who are in high demand and experienced, with plenty of job opportunities.


Overall, the world is changing, and Data Science is one of the most powerful tools for that change.

Data Science is more than just crunching numbers; creating a greater data science function within your company will benefit your organisation and future-proof its ability to change with its strategic objectives.

By embracing Data Science as an integral part of your business, you can ensure that your organisation is agile enough to keep up with technological changes and consumer behaviour.

Do you need help creating or managing your Data Science Team? Do you want to create a data-driven culture within your organisation? Or do you need to use Data Science Professionals?

Fill out the form below, and one of our team will be in touch to discuss your requirements further.

The Rise in Artificial Intelligence

The Rise in Artificial Intelligence

The Rise in Artificial Intelligence

From Robocop to The Matrix, there is usually a depiction of dystopia surrounding the idea of Artificial intelligence (AI). Over the years, the sentience on screen has presented technological advances and has bridged the gap between Science Fiction and Science Fact.

However, AI is here and is here to stay.

The depiction of Artificial intelligence presented in film and cinema is a somewhat dystopian perception. However, this isn’t true for reality. Artificial Intelligence is becoming, and will only become, more beneficial to business processes and success – challenging the perception seen on screen.

Not only this, but the development of AI has gained notoriety within recent years and has become a part of the modern cultural zeitgeist for its numerous and expansive capabilities, both beneficial to everyday life and business.

What is Artificial Intelligence?

But what is Artificial Intelligence? Artificial Intelligence, also known as AI, is a term which has undergone many iterations and evolutions in meaning. AI leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

While it can be assumed that there are several definitions of AI which have surfaced over the last few decades, John McCarthy, one of the ‘founding fathers’ of Artificial intelligence, defines AI as:

“the science and engineering of making intelligent machines, especially intelligent computer programmes. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”

Simply put, Artificial Intelligence is systems that act like humans.

How AI is Bettering the Development of The World.

As a result of the developments of Artificial Intelligence, it is arguably changing the world for the better.

Changing the workforce, AI is creating new jobs. The bleak view and argument of AI being a job killer is only one side of the coin. Yes, it is making menial, repetitive jobs obsolete due to Machine Learning and the automation of these jobs.

However, with the rise in AI, there are more emerging, engaging and less repetitive jobs. These jobs allow workers to have the opportunity to focus on the parts of the job that may be more satisfying for them to participate in whilst using more depth of knowledge to complete a complex task or job.

AI is allowing the world to become a smaller, more accessible place. No, not physically: but AI has the capabilities to bridge language divides. Whether you want to learn new languages or translate speech and text in real-time, AI-powered language tools, like Duolingo or Google Translate, are bridging the sociological and cultural gap in our worldwide community. From workplaces, classrooms, and whilst travelling, these real-time digital translations offer a means of understanding which may not have been possible without AI.

As AI becomes increasingly commonplace in the zeitgeist, it will only grow easier for your company to take advantage of its benefits. customers are quickly becoming accustomed to interacting with automated systems, so the ability to provide customers with simple, easy-to-use solutions may set you apart from your competitors.

AI has already revolutionised how many businesses operate and will continue to enhance Customer Service experiences and key business metrics like revenue and ROI in more ways than we can imagine.

Simulating the functions of human intelligence processed by machines, AI can make cars self-driving and identify risks more accurately in various industries. As well as this, AI can understand the data of a journey: notifying users about traffic and ETAs and giving the best route to users’ desired location – travel will only develop into an easier and more accessible experience.

AI Redefining the Traditional Boundaries of Art.

In addition to all of the points discussed above, Artificial Intelligence is redefining the traditional boundaries of art.

Being such a broad term, the definition of art is ver difficult to define. Taking the definition of art, art is:

the expression or application of human creative skill and imagination, typically in a visual form such as painting or sculpture, producing works to be appreciated primarily for their beauty or emotional power.

– Oxford Dictionary

From this definition, it can be argued that there is an explicit reference to the human when it comes to the creation of art. Therefore, sparking the debate of whether AI-generated art is true art due to the lack of the human during the creation.

The Modern Art movement was renowned for challenging this rational ideology and pushing the boundaries of what is considered art. Anything can be art is it is perceived as art. For example, an orange duct-taped to a wall can be considered modern art. The performative act of easing said orange could be considered performance, and thus performance art.

Keeping this in mind, it can be argued that AI-generated art can be seen as a subsection or new genre of art which has been around for years. Yet, the rise in discussions about Artificial Intelligence in the modern zeitgeist has only triggered the heightened popularity of this debate.

Computer Vision, a scientific field that deals with how computers gain high-lebel understanding from digital images or videos, as seen below, was the most popular brand of Machine Learning and AI art before the deep learning art explosion in the later 2010s and early 2020s.

AI Art systems have never stopped improving how they see and perceive the world. AI-generated art has advanced significantly throughout the last decade with the help of generative models and networks. These networks create images using datasets they’ve been trained on and develop their learning through Machine Learning (ML).

Similarly to AI technologies within the business world, AI-generated art is at our disposal now. With countless AI Art Generators available and free to use – you can simply input text prompts and the Artificial Intelligence develops a visual outcome based on the datasets it is continuously learning from – one of which was used to produce the image featured on this blog post!

AI Art Generators, like VQGAN+CLIP or DALL.E, are likely to continue to develop and evolve into highly sophisticated art engines. It is clear that AI is continuously growing, and who’s to say that the development of this will continuously impact the world of art and transform the perceived definition of what we believe art to be?

How AI is Changing Business.

Businesses of all sizes increasingly realise the importance of implementing Artificial Intelligence to achieve short-term and long-term goals.

For the greatest benefits, businesses should look at putting the full range of smart technologies – including ML, Natural Language Processing (NLP) and more – into their processes and products.

However, businesses that are new to AI can reap major rewards. AI technologies have the power to change the infrastructure drastically by:

    • Increasing work efficiency and customer satisfaction.
    • Reducing overall business costs.
    • Allowing for more rapid expansion and better consumer insights.
    • Reducing the risk of security attacks.

Within the business realm, the introduction of Ai also allows companies to gain a competitive advantage over their competitors in the market. With the increasing rate of technological innovations and advances in line with the exponential growth of varying AI technologies, the market dynamics will adjust accordingly.

The businesses which adopt and embrace these technological innovations will enable more flexible and modern strategies, consequently allowing for a significant increase in their chances of financial and overall organisational success.

Not only this, but as consumers increase their engagement within the digital marketplace, the wealth of behavioural data can produce meaningful insights into your target market or customers; location, job role, interest, and much more. With the implementation of AI technologies, this data and information will be used to inform your Sales and Marketing department’s campaigns, consumer experience and market insights. As a result, it creates more fiscal success.

Overall, using Artificial Intelligence within your organisation could yield fantastic benefits for those open to exploring the utility and emerging technologies as business tools.

The effect that these technologies produce is vast and expanding, meaning that as they develop, they will be able to benefit organisations in new and exciting ways.

However, it needs to be mentioned that although AI has made great technological advances, AI technologies don’t always perform best on their own., They are great at giving or even replacing lower-level, repetitive tasks; organisations often achieve greater success and best performance when humans and AI work together. Rather than replacing human capabilities, AI augments and improves upon human capabilities.

In short, when using Artificial Intelligence in your organisation, it can:

    • Boost revenue
    • Enhance customer experience
    • Create effective content and insights for Sales and Marketing Strategies
    • Create insightful analysis
    • Create a competitive advantage
    • Use Sales Forecasting to grow your business
    • Optimize your pricing
    • Improve cyber security
    • Save time
    • Reduce overall costs

Artificial Intelligence isn’t going anywhere. The technological growth of AI and the cultural awareness of AI within the modern zeitgeist isn’t stopping anytime soon.

From bettering your organisation’s operations, redefining the traditional boundaries of art, and self-driving cars, the capabilities of Artificial Intelligence are wide and expansive; they are only going to develop further and expand on human capabilities. Who’s to say that a person didn’t even write this blog post? Are robots officially taking over?

A business that doesn’t accept the capabilities of Artificial intelligence and implement it within their organisation will quickly struggle in the competitive landscape of business.

Want to learn more about how we can help you implement Artificial Intelligence into your organisation? Fill out the form below, and let’s discuss how we can help you make the transformation from Science Fiction to (Data) Science Fact.

Using Pebble Templates in WhereScape RED to Deal with Hard Deletes in an ODS Table

Using Pebble Templates in WhereScape RED to Deal with Hard Deletes in an ODS Table

Using Pebble Templates in WhereScape RED to Deal with Hard Deletes in an ODS Table. 


In a recent YouTube video, we discussed how to use Pebble Templates in WhereScape RED to Deal with hard Deletes in an ODS Table

Giving an overview of WhereScape RED, and the benefits it has for you and your organisation.

Then delving into Data Stores and how we expect them to work, especially around Historic Data Stores.

Enabling you to store data and capture changes to your data in a historic Data Store, WhereScape RED is a great piece of software to do this.

Also, we discussed how we have created our FREE Pebble Template which can be run as a custom procedure after loading the Data into the Data Store.

Our Pebble Template has been designed to identify and end or update the DSS_CURRENT_FLAG and consequently update the DSS_END_DATE in line with the setting within the Data Store.

To find out more, watch the video: