How Data Automation Will Optimize Your Organisation

by Tyler Bodys | May 9, 2023 | DataAutomation, News

How Data Automation Will Optimize Your Organisation

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

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

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

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

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

Data Automation can…

Save Time and Money:

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

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

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

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

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

Create Accurate and Fast Data:

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

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

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

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

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

Durability
Reliability
Scalability
ETL Capabilities

Create Better Documentation:

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

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

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

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

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

Automating your documentation process will:

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

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

Make all your Data in One Central Repository:

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

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

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

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

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

Make your Data Storage System Scalable:

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

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

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

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

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

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

Modernise your Legacy Data Warehouse:

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

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

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

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

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

Cost Reduction
Improved Profitability
Sales Projections
Standardized Processes
Improved Efficiencies

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

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

Increase Productivty within Your Organisation:

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

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

Create a higher level of efficiency with Data Automation.

Eliminate Data Silos:

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

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

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

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

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

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

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

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

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

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

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

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

Do more with less effort.

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

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

Start your Data Automation transformation.

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

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

High-Level Design Documentation

by Simon Meacher | Sep 15, 2021 | DataAutomation, News, WhereScape

Engaging Data Explains:

High Level Design Documentation –

Have you ever needed to create high-level documents of your data automation that explains a project/sprint within your WhereScape Red repository? Maybe so it looks a little like the above?

We recently worked with a client who wanted to represent the amount of change undertaken within a single project. They required something simple yet demonstrated the amount of change within each layer of the data automation.

Instead of creating something new, we re-used the WhereScape RED Overview design, WhereScape used to illustrate the design of the architecture.

A sample data solution shaped into the high-level design.

Engaging Data consultants worked with the client to create a solid naming convention and development standards. With this foundation and the metadata repository, we developed a script that produced an HTML document with details the client was looking for.

The idea continued to develop and now has options to include the following details:

Number of Jobs, Procedures and Host Scripts that support each layer.
Data Volume per object and summarised per layer
Processing time for each layer, with avg completion time & avg time to run

WhereScape RED and 3D speeds up development & documentation of the technical design. This solution utilises the metadata to create support or narrative documents for other business areas.

Build re-usable scripts, dashboards or reports for non-technical business teams & provide clarity around the technical function of your automation.

If you are interested in receiving a copy of the script that produced this report, please email simon.meacher@engagingdata.co.uk

Automated Deployment

by Simon Meacher | Jun 29, 2021 | DataAutomation, News, WhereScape

Engaging Data Explains:

Automated Deployment

Continuous Delivery using Octopus Deploy

With WhereScape RED

Many companies are looking to make Code changes/deployment easier. Often the ability to deploy code to production is surrounded by red tape & audited control. If you don’t have this, count yourself lucky!

Jenkins & Octopus Deploy are two, to name a few (see here), that are helping to automate the deployment of code to production. Allowing companies to adopt a continuous deployment/delivery approach.

For a long time, WhereScape RED has had its own method of automating deployment, using the command line functions.

Why Automate?

Using tools such as WhereScape RED allow elements of automating deployments; however, we know that companies like to use a common toolset for their code deployments; like having a single picture of all the deployments and, in most cases, realise that they want to release multiple code deployments on different platforms because RED doesn’t do everything.

Git?

No problem! There are several ways to do this. Our perfered option is to push the deployment application to the code store respository. Afterall, it is more practical to store the changes you want to push to Production and not every change to any objects, including those that are not meant for Production!

Can I do This Now?

WhereScape RED uses a command prompt file to send commands to the admin EXE. All changes will be applied to the destination database (via ODBC). Installation settings/config is set using XML & a log file is created as part of the process. The XML file contains the DSN of the destination database. Let’s come back to this point later. The XML contains all of the settings that are applied when deploying the application. Settings like Alter or Recreate Job. Please make sure you have this set correctly. You do not want to re-create a Data Store table to lose the history!

Permissions are important. The key to running the command line to publish changes to production is that the service account executing the commands has permissions to change the underlying database.

Integration with Octopus

Octopus deploy uses PowerShell as it’s common execution code. So we have adapted all of our WhereScape BAT files to PowerShell in order to get everything working.

Building a list of repeatable tasks within Octopus is easy & provides an opportunities to create a standard release process that meets with your companies standards/processes. Tasks like database backup, metadata backup and much much more!

It can even run test scripts!

We used a PowerShell script to create a full backup of the database, to be used should the deployment fail. With a larger database, this may not always be the best solution. Depending on your environment set up you may have options to use OS snapshots or other methods to roll back the changes. The good news is Octopus Deploy works with most technology, so you should find something that works for your technology stack.

Recently, we been playing with creating rollback WhereScape applications on the target data warehouse. This is great for restoring the structure of the objects quickly and easily. Reducing risk is a great value add!

Go, Go, Go!

Triggering the deployment was easy, we could set this up in many ways, but used “does the application files exists” trigger to get things started – until the humans learned to trust the Octopus process.

However, linking the release to Jira is just as simple. Imagine, you’ve completed development and want to sent the code to UAT. You click the button to update the ticket…….wait a few seconds…..and the code is deployed! It’s complicated to set up, but you get the idea.

Final Thoughts

Octopus is a great tool and the automation really helps to control the process of deployments. Coupled with WhereScape automation, this provides and excellent end to end solution for data warehousing.

If you are interested in CI/CD and WhereScape RED/3D, book a call us and find out how it could help your team.

Documenting the Modern Day Data Warehouse

by Simon Meacher | May 28, 2021 | DataAutomation, News, WhereScape

Engaging Data Explains:

Documenting The Modern Day Data Warehouse

When you’re operating a modern-day data warehouse, documentation is simply part of the job. But it’s not necessarily the easiest or most logistically straightforward part of the process, while also being important. Documentation is, in fact, invaluable to the continued development, expansion, and enhancement of a data warehouse. It’s therefore important to understand everything that is entailed in adequately documenting, in order to ensure that your data warehouse processes run smoothly.

Understanding your Audience

One of the first things to understand is who you are compiling the documentation for. Support, developers, data visualisation experts, and business users could all be possible recipients. Before you answer this question, you really need to fully understand the way that your organisation operates, and open the lines of communication with the appropriate departments.

A two-way dialogue will be productive in this ongoing process. This process of communication will then help ensure that you keep the documents in line with the design. This is vitally important, as any conflicts here can render the whole process less than constructive than is ideal.

And it’s especially vital considering how fast documentation moves nowadays. Everything has gone online, and is based on Wiki. Whether it’s Confluence, SharePoint, or Teams, all sorts of Wiki documents are being produced by businesses with the intention of sharing important information. These shareable documents are updated with increasing regularity, meaning it is important to get your strategy in place before beginning.

Different approaches to data warehouse design can also impact the amount of time that a document is live before being updated. If you are lucky enough to make weekly changes to your data warehouse, you will be making incremental changes to the documentation itself. Development teams spend hours on updating the documentation rather than doing what they are good at….developing data solutions! Naturally, minimising this where possible is always preferable.

Self-Service Business Intelligence

Documentation is also crucial in self-service business intelligence. The integration of private and local data in this area, into existing reports, analyses or data models, requires accurate documentation. Data can be drawn in this area from Excel documents, flat files, or a variety of external sources.

By creating self-service functionality, business users can quickly integrate data into what can often be vital reports. Local data can even be used to extend the information delivered by data warehousing, which will limit the workload that is inevitably incumbent on data management. The pressure on business intelligence can be quite intense, so anything that lessens the load is certainly to be welcomed.

Another important aspect of documentation is that it reduces the number of questions that are typically directed at the IT and data warehousing teams. One thing anyone that works in IT knows only too intimately is the vast amount of pressure that can be heaped upon them by both internal and external enquiries. Again, anything that reduces this will certainly be favourable.

The data warehouse team also has huge responsibility within any organisation. They are required to produce a vast amount of information for front-end business users, and getting documentation right can certainly assist with this process.

Importance of Transparency

One important aspect of documentation that can sometimes be overlooked is the importance of transparency. This works on every level of an organisation, with the importance of sharing everything related to documents absolutely vital. Once this level of transparency is implemented, people who understand the data deeply can improve the documentation, or suggest changes to the Extract, Transform, and Load (ETL) and Extract, Load, and Transform (ELT), if this is indeed deemed necessary.

Conversely, it’s also important to understand that not all technology is suitable for documentation. As much as businesses and organisations would love this process to be completely holistic, this is not always possible.

Thus, packages such as Power BI, QlikView and QlikSense, and even Microsoft’s trusty Excel, are not necessarily ready to be documented. These software packages can use data, but often do not have the ability to provide a document set that explain how the data is being used, and for what purpose. Recently, Power BI has taken steps to ensure that the app can help with data lineage, but this remains better suited to IT teams, as opposed to Business Users.

Attempting to document data across multiple technologies is tricky, but Wikis can provide IT teams with the ability to collate all of this information into a central hub of knowledge, making access much more logistically convenient.

Conclusion

Ultimately, IT departments, data warehousing teams, and report developers should all be encouraged to produce documentation that contributes to the overall aims of their organisations. Anything excessively technical is not good enough for modern business requirements, especially considering the importance of communication, and of ensuring that everyone within an organisation is acquainted with as much vital data as possible.

Modern-day technology makes this goal a reality, and this means that it is increasingly an expectation of end-users. Failing to prepare properly in this area could indeed mean preparing to fail, as organisations will simply have failed to meet the compelling desires of the market. It is therefore vital for documentation to be dealt with diligently.

Getting this piece right, will go a long way to help with data governance!

If you would like to know more about how Engaging Data help companies to automate documentation, please contact us on the below.

Big Data and DataVault

by Carl Richards | May 10, 2021 | Data Vault 2.0, DataAutomation, News, WhereScape

Engaging Data Explains:

Big Data and DataVault

Knowing how and where to find the needle more easily, and where in the specific haystack it resides

Big Data has been a hot potato topic for more than a few years now, and this phenomenon will play a central role in the future of commerce. Collecting, collating and comprehending Big Data will no longer be a matter of commercial interest; it will instead increasingly become a commercial imperative.

It should come as no surprise then that investment in technologies related to Big Data is already becoming almost ubiquitous. A report from NewVantage Partners, which collected executive perspectives from 60 Fortune 1000 companies, found that 97% of them invest in Big Data and AI initiatives. NewVantage also discovered that the vast majority of this investment (84%) was focused on deploying advanced analytics capabilities to enable business decision making.

Big Understatement

And when we use the term ‘Big Data’, it’s reasonable to conclude that ‘big’ is an understatement! For example, in 2018, Internet users generate approximately 2.5 quintillion bytes of data every day. That’s 912 quintillion bytes every year! And 90% of this data has been generated in just the last five years. The rate of growth and development of this curve is exponential.

Thus, it’s one thing to recognise the importance of Big Data, and quite another to be prepared for it. We’re talking about a veritable avalanche of information! In many cases, utterly unstructured information. Indeed, Forbes noted in 2019 that 95% of businesses cite the need to manage unstructured data as a problem for their business. Which, given the sheer scale of Big Data, is hardly surprising. Making the most of Big Data is not so much searching for a needle in a haystack; more like looking for a needle in a universe entirely comprised of haystacks.

This reality means that implementing the best business intelligence solutions will become essential. Dealing with the sheer volume of Big Data will demand this. And data warehousing is one element of this process that will be critically important. The analytical qualities delivered by this aspect of the overall Big Data management process will prove critical in the success of the efforts of companies to benefit from the information explosion.

Data Vault 2.0

That’s where Data Vault comes in. Data Vault 2.0 comprises a raft of sophisticated architecture and techniques that enable businesses to both store current and historical data in a singular and easily accessible location, along with the ability to create analytics based on this information. Data Vault is effectively a unique design methodology for large scale data warehouse platforms, ensuring that Big Data is dealt with more quickly, more efficiently, and more effectively.

Data Vault offers several advantages over competitors. The first reason for this is that it’s possible to convert any system to Data Vault determinations. This means that existing objects can be translated to Data Vault entities, and every single item will have a corresponding match in the new Data Vault architecture. Every main definition can then be mapped by hubs and every relationship between these via links. This means that the whole operation is more flexible and user-friendly.

Another significant advantage of Data Vault is its enhancement of agility. This is particularly important, as the ability of network software and hardware to automatically control and configure itself makes it easier to deal with the almost unfathomable scope of Big Data.

Smaller Pieces

Data Vault makes it possible to divide a system into smaller pieces, with each individual component available for separate design and development. This means every constituent part of the system can have its own definitions and relationships and that these can be combined at a later date by related mapping. This makes it possible to develop a project steadily yet still see instant results. It also makes managing change requests much more straightforward.

Another asset of the Data Vault approach is that it applies to numerous different systems. This means that separate sources can be transformed into Data Vault entries without any laborious procedures being involved. It is particularly advantageous in the contemporary climate, as almost every enterprise system relies on several different data types from various data sources.

The Data Vault modelling technique is thus adaptable to all types of sources, with a minimum of fuss. This makes it much more feasible to link different data sources together, making analysis more joined-up and holistic. It is well-known that being the entity that is the most adaptable to change is vital across a wide variety of niches, and this applies in the rapidly evolving data analysis environment.

But possibly the most compelling reason to choose Data Vault is that our offering provides companies with a method of standardisation. With Data Vault implemented, companies can standardise their entire DWH system. This standardisation enables members of the company to understand the system more easily, which is undoubtedly advantageous considering the innate complexity of this field.

Meeting the Needs

It is commonplace for complex and sophisticated solutions to be delivered to business users, which nevertheless fail to understand and adapt to the company’s actual requirements in that area. Everyone wants to show off their fancy piece of kit, but often developers aren’t as keen to listen! This can manifest for a variety of reasons. Still, the important thing to note is that Data Vault is designed to meet the requirements of the business, rather than requiring a business to reorganise itself to comply with the needs of the package.

This is important at a time when the dynamic complexity associated with data is escalating. Enterprise data warehouse systems must provide accurate business intelligence and support a variety of requirements. This has become a critical reality in a business marketplace in which the sheer volume of data being generated is overwhelming.

Data Vault solves these problems with a design methodology that is ideal for large scale data warehouse platforms. With an approach that enables incremental delivery and a structure that supports regular evolution over time, Data Vault delivers a standard for data warehousing that elevates the whole industry.

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