How to Create and Manage a Data Science Team

by Tyler Bodys | Apr 14, 2023 | Learn, News

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 experience, Data Science 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 provide 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

by Tyler Bodys | Mar 23, 2023 | DataAutomation, Learn, News

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

by Tyler Bodys | Mar 16, 2023 | DataAutomation, Learn, News, WhereScape

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:

Download the FREE Pebble Template

Why you need Data Specialists within your Organisation

by Tyler Bodys | Feb 27, 2023 | Learn, News

Why you need Data Specialists within your Organisations

Data is a specialism of IT and one of the fastest-growing industries. This results from the increasing importance of data’s role in our everyday lives.

Everything from our healthcare to our entertainment is driven by information produced by millions of people worldwide. Through the continuation of human life, it is arguable that data is, and will continue to, be created exponentially in more significant volumes and on a larger scale.

Therefore, the data landscape is vast and diverse. Consequently, becoming data literate within your organisation is a must and, when used successfully, can become the most valuable asset for a competitive advantage.

What is Data used for?

Data is the plain facts and statistics collected during the operations of a business, which can then be used to measure or record a diverse range of business activities, both internal and external. Whilst the data itself may not be the most informative, it is the foundation for all reporting and, therefore, crucial to your business’ success.

It needs to be said that there is a distinct difference between data and information. Data is raw facts and statistics. In contrast, information is data with a context that gives meaning and relevance.

Another way to look at information is: data that has been interpreted and presented in a more meaningful context that shows an informed narrative, allowing organisations to make better decisions based on facts.

Why is Data important?

As previously discussed, the data landscape is vast and diverse. With a significant amount of data at our disposal, organisations need to use this data. But what can you gain from data, and why is it so important?

1. Improved People’s Lives –

Data will help you improve thequality of your life for the people you support. Having the correct data will allow you to measure what matters and use this data to enhance your organisation and improve your employees’ work lives. Creating knowledge-based decisions through the use of the information which is presented within data.

2. Make Informed Decisions –

Data is knowledge. Good data provides indisputable evidence. Anecdotal evidence, assumptions or observations may feel correct, but they’re not. Data allows you to make informed decisions based on knowledge, information and insights.

3. Reduces Risk –

Data allows you to monitor the health of essential aspects of your organisation. Using data, organisations can respond rapidly to challenges before they become hugely detrimental to the whole organisation.

4. Get the Results you Want –

Data allows you to measure what matters. Measuring what matters grants you the ability to focus on the correct aspects of your business. Eliminate problems before they arise, as you can see the flaws and downfalls due to the information given as a result of using data effectively.

5. Back Up Your Arguments –

Data is a crucial component in telling you what is correct. Data will help you create a strong argument as to why something is working or something isn’t working. Illustrating a perspective supported by data will allow you to demonstrate how and why changes need to be made.

6. Stop the Guessing Game –

Data will help you explain decisions to your stakeholders, CXOs and others in your organisation. Whether your strategies and decisions are working, you can be confident that you’ve developed your approach based on good data, not guesswork.

7. Keep Track of Everything –

Good data allows an organisation to establish baselines, benchmarks and goals to keep moving forward efficiently. Data will enable you to measure performance and see if goals have been reached. And if they haven’t been reached, what the was reason behind this.

Why your Organisation needs Data Specialists.

With the exponential growth in data usage across countless organisations, the demand for Data Specialists is growing too.

Without the expertise of a professional who can utilise data effectively, data is nothing, and you cannot leverage the true power of it. It needs to be said that data is the critical driver of any organisation’s growth. Yet data needs to receive the attention it deserves – therefore, your organisation will reap the huge benefits of it.

In 2023, organisations need to be data-driven. However, many are only brushing the surface of this, employing individuals who have some understanding of data, yet they are specialists in this area. Consequently, what they are doing won’t be to the knowledge or skill level needed for great success.

The structure of demand for Data Specialists within organisations is rapidly changing and increasing due to the emergence of new methods and technologies for working with data. Therefore, it is essential to understand the areas of responsibilities, core competencies and skills of various Data Specialists to implement within your organisation. Leading to your organisation unlocking the value of data and leveraging its power. As a result, starting the journey to becoming a data-driven organisation – making every department effective in data usage.

Transform a Data Project into an Engaging Data Project.

Data specialists are workers who collect, store, manage and/or analyse, interpret and visualise data. Data specialists mainly comprise of two main groups:

Collect, Store and Manage Data – Data Engineers, Data Architects – to name a few.
Analyse, Interpret and Visualise Data – Data Analysts, Data Scientists – to name a few.

At Engaging Data, our Trusted Data Professionals know how to do it all. From visualising data to storing data, our team can help you unlock the true value of data and leverage it to benefit your organisation to create a competitive advantage.

Data Consutling –

Use data to transform your business.

Our Data Consultants provide a pragmatic and company-sympathetic approach to using data and analytics to transform your business.

Data Engineering –

Reduce time doing manual processes and increase the use of data in your business.

Our Consultants are here to help your team, whether it is a one-off job or a long-term engagement. Having a wealth of experience running projects using Data Automation software to fully automate data collection and processing, allowing data to be used in reports and self-service platforms.

Data Science –

Focus on the correct data to drive results.

Data and analytics are critical to the success of a contemporary business. Effective analytics require a blend of people, processes and technology, and understanding how one affects the other.

We provide strategic advice to help you make the right decisions and provide services to help you on your way.

Data Strategy –

Create a successful data-driven business.

All our strategies are bespoke and based on a solid foundation built from experience. This will provide your team with the resources and capabilities to build momentum and quickly gain results.

Data Visualisation –

Bring your data to life.

Taking your data and producing visuals allows your decision-makers to see, comprehend and decode what is happening with your organisation’s data and the inner workings.

Named after what we create for you, Engaging Data are here to help you unlock the true value of your data and push forwards towards overall business success.

The role of a Data Specialist, whether a Data Engineer, Data Scientist or Data Analyst, to name a few, is vital to any organisation.

Ignoring the importance of data will make your organisation crumble and left in the dust especially in the hugely competitive market of any industry. However, working with data and leveraging it will create a competitive advantage. All organisations should strive for a data-driven approach.

Working with a Data Specialist enables companies to leverage data and add undeniable value to their business. Not only this but it will create a data-driven environment and provide the information needed for effective decision-making.

Whether you are under-resourced for a data project, wanting to leverage data within your organisation, or have issues utilising your data effectively, fill out the form below and let us know your data struggles.

Our Data Professionals will help you transform your data into Engaging Data.

10 Tips for Making a Data Strategy Work for You

by Tyler Bodys | Feb 13, 2023 | Learn, News

Data Strategy is a complex subject, but one that can make all the difference in your business. Whether you want to generate more sales or improve customer support, you can do several things to get the most out of your data.

Here are our 10 Tips and Tricks for making a Data Strategy work for you!

Company Objectives AKA Top-Down:

1. Understand Business Goals

Knowing your company’s direction is essential before you start implementing a Data Strategy. You could have all the data in the world, but if it isn’t used correctly, what good is it?

Tip: You will often hear that data strategy is the ‘new business strategy’. And as such, you need to understand and analyse the information within your organisation to make informed decisions. You can do this by developing a data strategy that includes goals, issues and drivers for collecting data, as well as considering what types of data exist within your organisation – and where those sources may have come from.

2. Have a Data Strategy

You’re just wandering in the dark without a plan! Your employees must know about the importance of data and how it affects their work. By building a culture where data is valued, you can leverage its power to make better business decisions

Tip: Ensure that you have top-down buy-in from the top level of the company and that the Data Strategy is linked to business objectives. It will ensure that crucial business members are invested in the success of the data strategy if it means that the business objectives are successful!

People and Culture:

3. Build a Data-Driven Culture

You can’t manage what you don’t measure! your employees must know the importance of data and how it affects their work. By building a culture where Data is valued, you can leverage its power to make better business decisions.

Tip: it might not be worth collecting the data if you can’t answer the question, “what does it mean for me?”. Instead, think about what business problem you want to solve through the data, what do you need to know, or what you want to achieve.

Tip: Empower your team to make decisions based on data; you’ll be able to achieve the best results possible. The best way of doing this is by sharing information and allowing them to ask questions.

Tip: You can manage what you don’t measure! you must use the available data to make decisions about your company. Data can help you understand where there are gaps in some regions of your company, such as Sales and Marketing, and how they can be filled with more effective strategies.

4. Know your Data

It’s not enough to have data; you also need to know what it means. Look for patterns and trends in your data that can help you identify areas of opportunity and potential problems or issues that may pop up.

Tip: The key to success in data strategy lies in knowing what questions you are trying to answer and then identifying the ideal data required to answer those questions. Remember that nig data does not always mean the best data. It is important to think small. For example: by first understanding what questions your organisation whats to ask before working out what information you need to obtain.

Tip: It might not be worth collecting the data if you can answer the question, “what does it mean for me?” Instead, think about what business problem you want to solve through the data. What do you need to know, or what do you want to achieve?

5. Use Experts

Data is essential and can be highly confusing. From understanding how the data is created as part of the business process to sourcing data from the correct place – if you don’t have all the time or resources to devote to this, hire an expert who does! They will help you understand what your data means and how to make it work for you.

Tip: Understand the current data skills within your organisation. Will this enable or hinder your data strategy?

Tip: Can you train people to reduce the skill gap?

Process:

6. Data Governance

Data Governance is critical to data strategy success. Without proper leadership and policies that support information governance, your organisation can find itself overwhelmed by the complexity of managing data across its business units.

Tip: Start understanding who owns the data = from the moment it is created. These data owners know more about the data and how it can be properly used.

7. Change Management and Implementation

Data Strategy success hinges on change management. This is often overlooked in Data Strategy but is critical to a data strategy’s success. Change management has two parts:

– Ensure employees know why they should be excited about the new data strategy and how it will help them.

– Changing how things are done, so the new system works better after implementation.

Tip: There are always two sides to change, business and Technology. Make sure you coordinate both to avoid creating problems further down the line.

Technology:

8. Make Data Accessible

You don’t know anything without clear metrics! Your employees must know about the importance of data and how it affects their work. By building a culture where data is valued, you can leverage its power to make better business decisions.

Tip: Ultimately, reporting data aims to help you make informed decisions. Data Visualisations and presentations play an essential role in ensuring that the key insights from that data aren’t presented to the wrong people in the wrong way. At this stage, keeping your target audience in mind is perhaps one of the most important things to remember.

Tip: Once you understand what data is needed, how it will be turned into value, and how it will be communicated to the end user, there are several software and hardware considerations that you will need to address. What current analytic and reporting capabilities do you have? Should your legacy systems be supplemented with cloud solutions? How will the final reporting platform look when it’s deployed?

9. Using Technology:

You may not be able to buy all the technology you want on day one. Think about where your data is stored, processed and used. Different businesses have different concerns with processing efficiency and cost-saving storage. Others will have no option but to use the technology they already have.

Tip: Only collect, store and process the data needed for the data strategy’s primary objectives. Once successful, you can increase the scope to manage the remaining data.

Tip: Remove duplications as quickly as possible. If anything, this is a cost-saving exercise.

10. Keeping Up with the Times

Technology trends can significantly speed up a company’s data strategy. In recent years, due to COVID and the increased mobile nature of our daily lives, cloud technology and platforms have become more accessible. Data visualisation tools have also been accessed on more devices and device types.

Tip: Sign up for technology newsletters to understand the products’ development and roadmaps.

Your Data Strategy will not succeed if you do not have a plan to execute it.

This step-by-step guide will help you develop and implement your data strategy by identifying the key stakeholders, defining your KPIs and laying out the first steps for making things happen.

By working with the Data Professionals at Engaging Data, we will work with you to implement everything mentioned above and make your Data Strategy effective to make better business decisions and leverage the true power of data for your organisation’s longevity.

Accelerate Business Success Automatically: A Data Automation Overview

by Tyler Bodys | Jan 1, 2023 | DataAutomation, Learn, News

Accelerate Business Success Automatically:

A Data Automation Overview

Data automation is fast replacing conventional methods for building and maintaining various centralised data repositories, which organisations use to deliver data-driven value and insights.

Businesses can generate vast amounts of data taking on many forms. Structured and unstructured data from CRMs, HR systems, social media and websites – these varying forms and sources of data need to be stored somewhere – this is where Data Automation can help you!

Start accelerating business success automatically with Data Automation.

What is Data Automation?

Traditionally, data pipelines to move or transform data have been manually scripted by developers. not having a development standard, these processes allowed for developers to use their own coding style to apply different methodologies, either to an individual pipeline or a set of pipelines. This made amending code or supporting issues difficult to read through, interpret or alter. This problem was then scaled significantly by the number of developers in a team.

In addition, this can lead to poor process performance where the technology is not used to its fullest extent – or cannot process everything demanded.

Yet, Data Automation eliminates these problems – making them a distant memory after the implementation of Data Automation. Data Automation is collecting, processing and presenting data using automated tools instead of performing these tasks manually. Manually updating data presents a risk of being delayed and creates an additional workload for your team to complete alongside their day-to-day responsibilities.

Replacing manually coded scripts with a low-code or no-code tool helps your team focus on processing and modelling the data without spending hours writing code. With minimal human intervention, Data Automation will collect, transform, store and analyse data using well-designed methods, software and, where applicable, Artificial Intelligence. Eliminating the reliance on manual labour with bots that will do the job for you.

In addition, code standards can be applied to the data processing scripts, creating a consistent and easy-to-support data platform. Gone are the days of flawed, manually coded scripts.

Data Automation tools automate three key elements of Data Automation. This is known as ETL or ELT, depending on what tool you are using:

Extract – Data is exported from source systems like social media, emails, SQL Servers, etc. Into a staging area. This data could consist of various types and be pulled from most structured or unstructured sources.

Transform – Changing the data to fit the purpose. For example, transforming the data into a Star Schema ready for further analysis by Visualisation or Business Intelligence tools.

Load – The data is moved from the staging area into storage. For example, a Data Warehouse or a Data Lake.

What Tools are Available for Data Automation?

With a plethora of Data Automation tools in the market, there is a significant amount to choose from; some are more efficient than others. Some do a good job, and some do an excellent job.

At Engaging Data, to be relevant to the needs of our customers, we only partner with specifically selected Data and Information partners. This approach helps our clients bring true value in realising and delivering their data-led business change journey. Our Partners include:

Microsoft – Take care of what’s important. Automate the rest. Do more with less by streamlining repetitive tasks and business processes – increasing efficiency and reducing cost.

WhereScape – Accelerate with Data Automation. Rapidly deliver a meaningful and future-proofed data platform.

Matillion – Unlock new levels of productivity and get data business-ready faster.

Snowflake – Join the cloud. Execute your most critical workloads in a fully managed platform that capitalises on the near-infinite resources of the cloud.

VaultSpeed – Delivering unparalleled automation of upfront data warehouse design and development. Fully compliant with Data Vault 2.0.

Why You Need Data Automation.

As previously mentioned, Data Automation helps improve productivity around the use of data within your organisation. Some other benefits influence improved Data Quality, simpler Data Cleaning and optimized Data Transformation.

Improved Data Quality –

As a result of datasets being standardized in accordance with formats and schemas, you can use Data Automation not detect and fix any records that don’t adhere to the format. Data Automation helps you improve data integrity and quickly detect errors.

Simpler Data Cleaning –

Data Cleaning transforms raw data, usually unformatted and unstandardised, into a significantly more suitable format for data analysis. Generally, the steps taken in Data Cleaning are repeatable, thus frameworks and standards for future datasets.

Automating these steps, using scripts and schedules to make new data match existing schemas and running steps on a recurring basis whenever new data is needed or imported.

Optimized Data Transformation –

The process of changing datasets from one format to another, Data transformation involves checking the datasets to ensure its in the correct format in the destination repository. Ensure that the data quality is good and checking the dataset follows the set standards for your organisation’s requirements or the data’s destination.

By automating Data transformation, it will help accelerate processes as you will need to manipulate, transform or analyse the data, and it is already in a standardized format.

Not only does Data Automation grant the large benefits discussed above, but Data Automation also allows for these general business benefits:

Enables Better Decision-Making – Businesses can react rapidly to the data’s narrative. Allowing for decisions to be made quickly with more data-led insights

Reduced Risk of Human Error – Manual data entry is prone to human errors that can cause Data Quality issues and affect reporting accuracy.

Improved Operation Efficiency – By setting up the rules and processes with software, you can easily audit and redefine these processes. You can quickly identify any broken or unnecessary workflows.

More Efficient Workforce – Teams no longer need to spend their precious time and advanced skillsets on mundane admin. Instead, they can add value elsewhere as their skill sets are appropriately optimized.

Reduces Operational Costs – You can get substantially more done with the same resources: minimal investment whilst maximising return.

Scalability – Automation software is ready to scale with your business, so you can utilise its benefits regardless of where your business is now and how much it grows in the future.

How Engaging Data can help You with Data Automation.

At Engaging Data, we understand that everyone needs data built efficiently to gain value quickly.

Using innovative Data Automation tools, we help you seamlessly integrate your data into accessible and secure platforms. Building data for a purpose, we only process relevant information to achieve your goals.

Do more with less effort.

Let us help you enable your teams to do more, eliminate human error and produce a higher standard of work with minimum effort.

Automate your data now.

Fill out the form below, and work with the experts behind Data Automation.

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