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?
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.
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.
How is CI/CD (Continuous Integration / Continuous Delivery) Used to Modernise a Data Warehouse?
A specially designed type of data management, a data warehouse is a system that has been built to enable and support business intelligence and analytics. For example, a data warehouse will read large amounts of data to understand relationships and trends within an organisation.
Data warehouses contain large amounts of historical data and are intended to perform queries and carry out analysis. The data within a data warehouse is often derived from various sources, such as application log files and transaction details. You’re centralising and consolidating large amounts of data into one location by utilising a data warehouse. Over time, this data can be invaluable to data scientists and business analysts, allowing informed business decisions to be made using valuable business data insights. As this data record builds, a data warehouse is often considered an organisation’s single source of truth.
Analytics and data have become indispensable to allow businesses to stay competitive, and companies rely on reports, dashboards, and analytic tools to extract data, monitor business performance, and support future business decisions. The power behind these processes are data warehouses, which store data efficiently to minimise the input and output of data and deliver query results quickly.
What’s the Architecture of a Data Warehouse?
A data warehouse architecture is created in tiers. The top tier is the front-end, where results are presented through reporting, analysis, and data mining. The middle tier is where the analytics engine used to access and analyse data sits. The bottom tier of the architecture is the database server, where data is loaded and stored.
Data is stored in two types of ways:
Data accessed regularly is stored in fast storage, like an SSD drive.
Whilst data that is less frequently accessed is stored in an object store.
The data warehouse is competent at differentiating between the two types of data. It will automatically ensure that frequently accessed data is available in the fast storage option, thereby optimising query speed.
So, how does a Data Warehouse work?
A data warehouse might contain multiple databases, and within each database, the data will be organised into tables and columns. Within each column, you can then define a description of the data, for example, integer, string, or data field. Tables can be organised within schemes, using a similar structure to files and folders. So, data is added to a data warehouse, it’s then collected and stored into various tables defined by the schema, and query tools utilise the schema to identify which data tables to access and analyse.
What are the benefits of using a Data Warehouse?
As we mentioned above, there are multiple benefits to using a data warehouse, for example, making more informed business decisions based on data and insight. Here are some of the additional benefits of using a data warehouse:
Consolidated data from many sources
Historical data analysis
Data quality, consistency, and accuracy
Separation of analytics processing from transactional databases and improves the performance of both systems
What’s a Modern Data Warehouse?
Across your organisation, multiple teams and users will have different needs for a data warehouse. Traditional data warehousing can’t always keep up with the demands of rapidly growing volumes of data, processing workloads and analysing data. In contrast, a modern data warehouse architecture addresses the different needs of your organisation by providing a way to manage everything you need with various integrated components that all work together.
A modern data warehouse would typically include:
A streamlined database that simplifies the management of all data types and provides different ways to use data across your organisation
Self-service data ingestion and transformation services
Support for SQL, machine learning, graph, and spatial processing
Multiple analytics options that make it easy to use data without moving it
Automated management for simple provisioning, scaling, and administration
Ultimately, a modern data warehouse can efficiently streamline data workflows in a way that traditional data warehouses can’t. This allows everyone to perform their jobs more effectively and efficiently, from analysts and data engineers to IT teams and data scientists.
If you have a lot of data, and multiple teams that all need to access it, then modernising your data warehouse should be a key component of your data strategy. But how can you update your data warehouse? This is where CI/CD, which stands for Continuous Integration (CI) and Continuous Delivery (CD), comes into play. CI/CD creates a faster, more precise, and overall efficient way of combining the work of multiple teams into one streamlined product. For example, in app development and operations (DevOps), CI/CD would streamline coding, testing, and deployment by creating a single space for storing work and tools, thereby consistently combining and testing code to ensure it works.
What is the CI/CD pipeline, and how can it support modernising a Data Warehouse?
By utilising a CI/CD pipeline, any software development or engineering processes that combine automated code building with testing and deployment, you can deploy new and updated software safely and precisely.
In other words, a CI/CD pipeline is the behind-the-scenes plumbing of your data and analytics, making your life easier and your work more consistent. Here at Engaging Data, we have developed a solution that integrates WhereScape 3D & RED with CI/CD and DevOps pipelines in one ecosystem to modernise the world data warehousing. If you’d like to know more about our CI/CD pipeline, take a look here.
Because a CI/CD pipeline isn’t just a linear process, it allows DevOps teams to write code, integrate it, test, deliver updates & releases, and make changes to the software in real time. In addition, the ability to automate critical parts of the CI/CD pipeline allows development teams to work more efficiently and more effectively and improve other DevOps metrics.
What are the benefits of the CI/CD pipeline in modernising a Data Warehouse?
The most significant benefit of a CI/CD pipeline is the automation of releases from the initial testing to deployment. Additional benefits of the CI/CD pipeline for DevOps include:
Automated testing makes the development time more efficient; CD and automation mean that a developer’s changes to a cloud application could go live within minutes.
Thanks to faster, more efficient testing and development, less time is needed to be spent in the development phase, therefore reducing cost.
The CI/CD pipeline is a continuous code, test, and deploy cycle. Every time code is tested, developers can react to feedback and improve the code.
A CI/CD pipeline allows a more collaborative and integrated process with everyone across the organisation who needs to access the data warehouse.
If you have any questions about a CI/CD pipeline or the deployment process, we have diagrams of the flow here.
Conclusion
Overall, a modern data warehouse utilising CI/CD should be an essential part of your data strategy if your organisation has multiple touchpoints requiring access to data, insight, and analytics.
Do you have any questions about modernising your data warehouse? Or about creating and implementing a CI/CD pipeline? Our expert team of data specialists can implement a modern data warehouse for your organisation.
Fill out our form below, and one of our team will be in touch.
