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 it’s 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 needing 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?
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.
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.
