From powering day-to-day operations to driving strategic decision-making, technology plays a pivotal role – and with this so does your IT and Data Management.
However, not all businesses are equipped with the resources and expertise needed to manage their Data and IT effectively.
Let’s explore the challenges faced by businesses with small IT and Data Teams and how you can help your business thrive again, with some small tweaks and a different approach.
The Downside of Small IT and Data Teams:
Inefficient Operations
Small IT and Data teams often struggle to keep up with the demands of daily operations.
Overloaded employees may find it challenging to respond promptly to issues, resulting in downtime and customer dissatisfaction. Productivity can take a hit, impacting the bottom line.
Limited Expertise
With small teams, it’s challenging to maintain expertise in every area of technology. This can lead to suboptimal technology decisions, outdated systems, and inadequate security measures. The risks associated with these gaps can be significant, including data breaches and compliance issues.
Incomplete Data Management
Effective data management is essential for business growth. Small teams may find it difficult to harness the full potential of their data. This not only hinders decision-making but also puts businesses at risk of losing valuable insights and opportunities.
industry.
The Cost of Not Changing Your Ways:
Lost Opportunities
Businesses with small IT and Data teams often miss out on valuable opportunities for growth and innovation. They may struggle to adopt new technologies or adapt to changing market conditions. This can result in stagnation and lost market share.
Increased Costs
Paradoxically, efforts to cut costs by maintaining small teams can lead to higher expenses in the long run. Inefficient processes, downtime, and costly emergency fixes can erode profitability. What initially seemed like a cost-saving measure can end up being a financial burden.
How We Can Help You:
Expertise and Resources
By partnering with the crew at Engaging Data, your business will gain access to a wealth of expertise and resources. Our team is equipped with specialised knowledge, cutting-edge technologies, and a deep understanding of industry best practices.
Improved Efficiency
Our services are designed to streamline operations and boost productivity. With responsive support and proactive maintenance, we ensure that your technology infrastructure runs smoothly.
Say goodbye to frustrating downtime and hello to enhanced efficiency.
Comprehensive Data Management
We take data management seriously. Our approach includes robust security measures and compliance protocols to safeguard your data. We also help you unlock the potential of your data, turning it into a valuable asset for making informed decisions.
To conclude, small IT and Data teams can unintentionally hamper business growth and incur hidden costs – this is not great!
However, there is a way out.
By partnering with the Data Nerds at Engaging Data, you can unlock the full potential of your technology infrastructure and data assets.
Don’t let limitations hold your business back.
Let’s book in a call and help your business thrive in the digital age.
Fill out the form below, let’s have a chat to discuss your limitations, problems and how we can overcome them together!
Leveraging Analytics in the Era of Digital Transformation
In the fast-evolving landscape of the financial services industry, a new era has dawned—one that revolves around data.
Data-driven finance is no longer a mere concept; it’s a fundamental shift that is reshaping the way financial institutions operate and make strategic decisions.
In this era of digital transformation, data has emerged as a potent currency, enabling financial organizations to gain insights that were previously unimaginable.
In this blog post, we delve into the world of data-driven finance, exploring its significance, benefits, challenges, and the promising future it holds.
The Role of Data in Financial Services
Traditionally, financial services have always relied on data and information to drive their operations. From analysing market trends to assessing customer creditworthiness, data has been the backbone of decision-making.
However, with the advent of digital transformation, the importance of data has surged to new heights.
Digital transformation is the process of adoption and implementation of digital technology by an organisation to create new or modify existing products, services and operations. The goal for its implementation is to increase value through innovation, invention, improved customer experience and efficiency.
As financial services become increasingly complex and competitive, the need for accurate, timely, and relevant data has intensified. In this data-centric era, financial institutions are not just making decisions; they are uncovering insights that can redefine the entire industry.
Key Benefits of Data-Driven Finance
By harnessing the power of data analytics, financial institutions can:
Consider companies like Monzo and Starling, which utilized data-driven approaches to tailor personalized financial solutions, ultimately setting them apart from their competitors.
Data Sources and Collection Methods
The array of data sources available to financial institutions today is astounding.
Customer transactions, market data, social media interactions, and even sensor data from Internet of Things (IoT) devices.
All of which contribute to the wealth of information at their disposal.
Modern data collection methods have expanded to include mobile apps, online platforms, and interconnected devices.
This data influx not only broadens the scope of analysis but also presents new challenges in terms of data quality, integration, and privacy.
Analytics Techniques in Financial Services
Data analytics techniques have evolved hand in hand with the digital transformation of finance.
Descriptive analytics offers insights into historical trends, while predictive analytics helps anticipate future outcomes.
Prescriptive analytics goes a step further, recommending optimal actions based on data insights.
Machine learning and artificial intelligence are driving breakthroughs in analysing vast datasets and uncovering patterns that were previously hidden.
These techniques enable financial institutions to make more accurate predictions, streamline processes, and discover untapped opportunities.
Use Cases of Data-Driven Finance
The impact of data-driven analytics is felt across various sectors within finance.
In banking, institutions leverage data to personalize services, detect fraud, and enhance risk assessment. Investment firms use data-driven insights to inform portfolio management and optimize investment strategies.
Insurance companies employ data to assess claims and tailor coverage plans. Lending institutions rely on data analytics to evaluate creditworthiness and streamline loan processing.
Each of these use cases showcases how data-driven finance is transforming traditional practices and opening new avenues for innovation.
Challenges and Considerations
While the benefits of data-driven finance are clear, challenges must also be acknowledged.
Data privacy concerns, security risks, and regulatory compliance are critical issues that financial institutions must navigate.
The ethical use of data is paramount, as biased algorithms can perpetuate inequalities. Addressing these challenges requires a balanced approach that prioritizes transparency, accountability, and adherence to regulatory standards.
Building a Data-Driven Culture
Embracing data-driven finance entails more than just adopting advanced technologies – it requires a cultural shift within financial institutions.
Leadership support, training programs, and cross-departmental collaboration are essential elements of building a data-driven mindset.
Organizations need to foster an environment where data is valued, and analytics is integrated into decision-making processes at all levels.
The Future of Data-Driven Finance
The journey of data-driven finance is an ongoing one, marked by continuous innovation and exploration.
As technology advances, new opportunities emerge and hold the potential to revolutionize data processing and analysis, while advanced analytics techniques like deep learning promise even deeper insights.
Data marketplaces may reshape how financial institutions access and exchange data, fostering collaboration and accelerating industry progress.
The rise of AI-generated financial insights and predictions could further amplify the capabilities of data-driven finance.
In conclusion, data-driven finance stands as a cornerstone of the digital transformation sweeping through the financial services industry.
By leveraging data analytics, institutions are navigating complexities, uncovering opportunities, and forging pathways toward greater efficiency and customer-centricity.
As this era continues to unfold, staying informed about evolving data trends and embracing data-driven strategies will be pivotal for both financial organizations and individuals seeking to thrive in this dynamic landscape.
Start your digital transformation before it’s too late and you lose out in the competitive marketplace and are left behind.
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 Digital Transformation is Reshaping Financial Services
The financial services landscape is undergoing a profound transformation—one driven by the relentless pace of technological advancement.
The concept of digital transformation has taken centre stage, revolutionised traditional practices and reshaping the way financial institutions operate.
As the digital era continues to unfold, the implications for financial services are vast and far-reaching.
In this blog post, we’ll explore the key ways in which digital transformation is reshaping the financial services sector and discuss the critical points that highlight its significance.
The Digital Disruption in Finance
The financial services industry, which has long been anchored in conventional practices, has experienced a seismic shift over the years. From manual ledgers and face-to-face transactions to the digital realm, the evolution has been nothing short of transformative.
However, this shift is not limited to the finance sector alone. Industries across the spectrum, from retail to healthcare, have undergone their own digital transformations.
As we delve into the digital disruption in finance, we’ll examine how these shifts have paved the way for innovation and evolution within the sector.
Digital Customer Experience
One of the most tangible impacts of digital transformation in financial services is the enhanced customer experience.
Gone are the days of waiting in line at brick-and-mortar banks or enduring lengthy phone calls for simple inquiries. Digital transformation has introduced a new era of convenience, with online banking, mobile apps, and seamless transactions becoming the norm.
The customer is now empowered to manage their finances on their terms, anytime and anywhere.
Furthermore, the importance of personalization and user-centric design cannot be overstated.
Financial institutions that prioritize these aspects are better positioned to attract and retain customers in an increasingly competitive landscape.
Fintech Innovations and Startups
The rise of fintech startups has injected a fresh wave of innovation and competition into the financial services sector.
Peer-to-peer lending, AI-advisors, blockchain technology—the list of fintech innovations is both impressive and extensive. These innovations have not only expanded the array of services available to consumers but have also challenged the dominance of traditional financial institutions.
The traditional hierarchy is being disrupted as fintech startups pave the way for more inclusive and efficient financial solutions.
Data-Driven Decision Making
In the age of digital transformation, data reigns supreme.
The utilization of data analytics, powered by big data and artificial intelligence, has transformed decision-making processes within financial services. From risk assessment and fraud detection to investment strategies, data-driven insights provide a level of accuracy and efficiency that was previously unattainable.
This shift towards data-centric decision-making is not only enhancing operational effectiveness but also redefining the landscape of risk management and financial planning.
Efficiency and Cost Savings
Operational efficiency is at the heart of every business’s success, and digital transformation has become a key driver in achieving this efficiency within financial services.
Through automation, process digitization, and the reduction of manual tasks, financial institutions are streamlining their operations and increasing their capacity to handle complex tasks.
Consequently, this efficiency translates into significant cost savings and improved resource allocation, allowing organizations to redirect their focus towards innovation and strategic growth.
Regulatory Challenges and Security
As financial services embrace digital transformation, they must also navigate a complex web of regulatory challenges and security concerns.
With increased digital interactions come heightened vulnerabilities, necessitating robust cybersecurity measures to protect sensitive financial data.
Real-world examples of security breaches serve as stark reminders of the importance of maintaining a vigilant stance against cyber threats.
The Road Ahead: Challenges and Opportunities
While the benefits of digital transformation are vast, the journey is not without its challenges.
Financial institutions must confront the need for a cultural shift—one that embraces technological evolution and fosters a learning mindset. Upskilling employees to adapt to new technologies is paramount to ensuring a smooth transition.
Despite the challenges, the road ahead is paved with opportunities. Digital transformation opens doors to growth, innovation, and improved customer relationships.
Organizations that leverage these opportunities stand to flourish in the ever-evolving landscape of financial services.
In conclusion, digital transformation is not merely a passing trend; it is an imperative for survival and growth in the financial services industry.
The changes brought about by digital transformation are profound and have the potential to reshape the industry as we know it. From enhancing customer experiences to fostering innovation, the impacts are far-reaching.
As the financial services sector navigates this transformative journey, it is essential for both businesses and consumers to stay informed about emerging technologies and their potential impacts on financial decisions. Embracing the digital future is not an option – it’s a necessity.
Start your digital transformation before it’s too late and you lose out in the competitive marketplace and are left behind.
Being a critical component of any data-driven organisation, a Data Warehouse is a necessity.
However, you’re probably wondering why your Data Warehouse is the biggest pain in your life, or maybe you don’t even have one yet?! Unfortunately, there isn’t one quick fix to solve all your problems – as nice as that’d be!
But it’s time to get real – most Data Warehouses are crap. And, ultimately, yours could be one of them.
A poorly designed or maintained Data Warehouse can cause serious problems for your business, becoming a hindrance rather than a help. Which we’re sure you don’t want, and we certainly don’t want that for you!
But why are you dealing with a crap Data Warehouse? It doesn’t have to be this way; you shouldn’t be suffering!
Keep scrolling (and reading, obviously) and you will learn why your Data Warehouse is failing and how to eliminate your Data Warehouse pain and experience Data Warehouse pleasure.
With a mass of legacy code built up over a significant amount of time, by an amalgamation of different developers, consultants, freelancers or even monkeys – your Data Warehouse has no coding standards. Each individual has their own way of working and different approaches to answering your technical problems, which causes points of concern.
Arguably, this doesn’t solve any of the technical problems you have encountered within your Data Warehouse. It just raises more problems!
With the different processes, different ways to answer problems and no coding standards, from these individuals, it makes the support process a massive pain. Resulting in your having to sift through the code, try to understand what is happening, why it is happening, how it is happening, where it is happening… you get the picture.
Consequently, it takes hours to try and figure out what caused the problem in the first place! And in that time, you’ve probably encountered even more issues which you need to spend even more time trying to fix. It’s a vicious cycle, to be honest!
As if this wasn’t enough, the support overhead for this is massive! Having to do these manual and repeatable tasks is time-consuming and a huge strain on your resources. Let’s face it, do you really enjoy doing these mundane tasks?
Repetitive tasks kill productivity.
The more time that is being spent on doing mundane, repeatable tasks the less time you spend on the things that matter and benefit your organisation’s growth and overall success.
Why are you doing repeatable tasks? You could spend your time wisely, and more efficiently, by building out new data features and data products that will help your business advance and contribute to the building of a data-driven culture. Oh wait, you don’t have the time because of these mundane, repeatable tasks!
Why waste time and money when you can automate these processes? Seems simple, right?
Data is clearly vital to your organisation, which is why you need to stop wasting time processing data and struggling to build new data processes. You need to start spending your time being laser-focused on delivering data-driven analytics.
This is where we can help you!
At Engaging Data, our experience with Data Automation tools has helped us build many different data platforms for our clients.
Working closely with you, we’re here for a good time not a long time! Taking your data and requirements and transforming them into usable assets in a matter of weeks – not months.
If you don’t experience this at the moment and your Data Warehouse is failing or if it is the biggest pain within your organisation, we have something for you!
Start experiencing Data Warehouse pleasure instead of Data Warehouse pain.
Get our FREE DOWNLOAD on the 10 Reasons Why Your Data Warehouse is Killing You and stop your Data Warehouse from failing!
In this download, there is even more insight and information into why your Data Warehouse is failing. Go on, download it!
Or you could just keep your struggling with your crap Data Warehouse – it’s up to you!
FREE DOWNLOAD: 10 Reasons Why Your Data Warehouse is Killing You!
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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.
