by Tyler Bodys | Nov 8, 2022 | Learn, News
The Power of Measuring What Matters and Using Data to Grow.
The world of business has changed drastically in recent years.
A business can no longer survive by simply being good at what they do; instead, they need to be great at it. To be great, you must constantly learn from all aspects of your organisation and use that information to make improvements wherever possible.
Data is the key. Without using data, your organisation will never be able to reach its full potential.
With Engaging Data’s help, we can support you in unlocking the power and value of data to create successful business development and longevity.
Without using data, you will have a problem.
Data is the key to success. Without the value of data within your organisation, it will be detrimental to the development and overall growth.
If you want to know anything in business, you need data. It allows you to predict future problems and solutions, find customers and develop new ideas. Without data, there is no way of doing any of these things!
Gaining insights without data is difficult. You might think that you know your problem and how to solve it, yet this may not be the case. You may not know whether your solution works or is good until you implement it in practice and measure its results.
Without data, there’s no way to tell if the solution worked or not. You also won’t be able to accurately measure the outcome of a solution or activity without the insightful information that data holds.
Without data, you cannot define the problem or the magnitude it can cause. You can’t even begin to measure its impact on your business. So how will you be able to tell if your solution has made a difference? It would help if you had facts, figures, and information gained using data to help guide decision-making.
Decision-making is difficult at the best of times, but without insightful information – gained through data – this will become even more difficult. You will not be able to make informed decisions about the direction of your company or how well it is doing against its goals.
Data is essential because it allows you to see insightful patterns and trends in your business. It will enable you to track performance over time or compare the results of different actions. Data is a great tool to have in your tool belt. Like any tool, you can use data to make something good or bad. However, when used effectively and in the right hands, you can make better decisions and achieve better results.
For an organisation that doesn’t use data, your competitors who use data are and will always be one step ahead. A data-driven organisation has information and deeper insights into how well these things work and how they can improve or change them entirely to ensure that the overall organisation continues to work well for them in the future. Therefore, the data-driven organisation can stay ahead of any competition while providing excellent customer satisfaction through increased awareness and understanding of what customers want or need from their product offerings.
Whereas an organisation that isn’t data-driven doesn’t have any of this information and insight, working with gut feeling and guesswork – is not a great business strategy.
Measuring what matters and using data avoids problems and provides practical business development.
Data is also a powerful tool for making your company more efficient. When you measure what matters, you can use that data to improve internal operations, reduce costs and create an overall better organisation.
For example: measuring how long each employee spends on their tasks reveals that one person consistently takes longer than others on similar projects. By allocating some of their time towards another job, they might find more success while reducing wasted effort elsewhere in other areas — leading to increased productivity overall.
Creating a competitive advantage, measuring what matters and using data can allow a deeper understanding of your customers and clients. Meeting needs and expectations so that you can provide products or services that meet them in ways competitors don’t.
Data matters. Use it.
Data matters and will only become more critical over time; always be one step ahead of your competitors when you use data.
Data is everywhere, and you can use it for various purposes. Data can tell you about your customers’ habits, how to optimise your website for optimum conversion rates, or even how to improve your product development process, to name a few.
Organisations that measure what matters and use data to grow also:
- Create Better Products.
- Create Better Services.
- Create Better Business.
- Create Better Sales and Marketing.
When you have data in front of you, it’s easier to see the big picture. You can quickly make sense of all the new information and see what’s important, which will help you make better decisions. You also use data to make a case for something your organisation needs to address.
Data shows everyone exactly why they should make changes instead of relying on gut feelings and guesswork.
The value of data within any organisation is undeniable. Approached by one of the largest Private Banking and Asset Management groups, we supported a client in becoming a data-driven company with our Data Strategy Services.
Helping our client with Data issues like:
- Siloed Data
- Data Extraction
- Untrusted Data
- Disconnected Data
- Resource Intensive Procedures
As a result of implementing a Data Strategy and becoming data-driven, our client could take their data, which was previously siloed and used ineffectively, and transform it into a huge business asset.
You can read our Case Study in full here.
Conclusion
Data is the key to success; in every business and industry.
Measuring what matters and using data to grow your organisation is necessary for the strength and success of every aspect of your organisation and, therefore, overall business success.
Want to learn more or have any questions about how you can start measuring what matters and growing using data?
Get in touch, and our team of Trusted Data Professionals can discuss how we can help and support you with a bespoke solution for your specific problem and requirements.
Alternatively, if there is a specific service you wish to discuss, let us know and find out more here.
by Tyler Bodys | Oct 24, 2022 | Learn, Matillion, News
3 Dangerous Outcomes of Information Gaps
You’ve probably heard us talk about Information Gaps. Information Gaps occur when analytics break down in the cloud, due to implementation challenges, lack of data synchronization or an underdeveloped data culture at your organization. Sometimes information gaps are a result of no data in an organization, but most often enterprises have plenty of data: more than they can handle, in fact. The breakdown occurs when there’s an absence of a smooth data supply chain, where data technology, people and processes work together to keep data moving through an organization.
But what happens when there are Information Gaps?
The Three Dangers of Information Gaps
Information gaps can ruin decisions and careers. They cause automation failure and revenue-leaking inefficiencies. This lack of context and data together can have dire consequences for businesses trying to compete in digital economies.
No. 1 – Flying Blind
If data is simply not available for use in decision making and prioritization, opinions rush in to fill the void. So many opinions, in fact, that organizations often thrash about. Strategies are often determined by those who make the most noise or wield the most power. An organization powered by loudly voiced opinion in a fast-moving digital marketplace is doomed to failure.
No. 2 – Data without Context
This is perhaps the most tricky Information Gap of all. In this scenario, data can easily be molded into opinions masquerading as facts. When the discipline of transforming raw data into contextual information is performed incompletely, or the context comes from the inside of someone’s brain, raw data is spewed across the organization under the guise and backing of a formal analytics program. This creates an extremely dangerous scenario where inaccurate data can be bent to support the arguments of partisan perspectives without warning. This environment produces some of the world’s worst business blunders.
No. 3 – Unskilled and Untrained Self-Service
this onslaught of data has reduced the capacity for analytics in most organizations precisely when it is needed the most. Traditional analytics programs–and many data teams–run on highly specialized data handling skills in the hands of a few. They’re not equipped to handle the “Three D’s” of modern cloud analytics.
As data teams struggle under the burdens of running modern analytics programs, leading to longer delays and seemingly intractable team bandwidth issues, today’s digital savvy workforce is prone to bypassing the official analytics program altogether.
In this mode, digital-native workers simply download data directly from sources and try to stitch it together manually. The result is yet another form of data masquerading as information, an Information Gap. The charts get created and they seem conclusive. But deep underneath them lies a dataset which was not curated, cleansed and enriched by the skills of an experienced analytics professional. And worse, these datasets proliferate in silos, often propagating several half-baked versions of the truth.
How do you prevent Information Gaps?
Enterprises can help prevent Information Gaps from wreaking havoc on the business by ensuring that data teams across the business and data end users have access to the shared, secured, connected data in the cloud and the right tools and techniques to take advantage of it and generate real business value. Cloud-native data integration and transformation of data helps companies maintain that shared source of data and a healthy data supply chain to ensure that the right data comes together to unlock amazing insights into customers, operations, and future innovations.
Learn more about how Information Gaps can affect your business.
How do you spot Information Gaps, and how do you close them?
To learn more, be sure to read our ebook, Close the Information Gap: How to Succeed in Cloud Analytics.
by Tyler Bodys | Sep 26, 2022 | Learn, Matillion, News
What is an Information Gap?
5 Signs You Might Have One (Or More)
Today’s businesses aspire to be “data-driven,” but what does that really mean? In today’s terms, a data-driven business is one that uses data across the organization to:
- Quickly iterate existing product lines to address new markets
- Optimize supply chains to meet dynamic geopolitical conditions
- Providing personalized experiences to consumers at an enterprise scale
- And more…
The key is a strong data and analytics culture, producing vital information that informs decision-making and behavior throughout the business. But a failure of analytics can open up information gaps that divide teams and silo data, leaving enterprises in the dark and struggling to catch up as their data-savvy competitors seize new opportunities and widen their lead in the market.
Mind the Information Gap
Data alone is not the same as information.
Information = Data + Context
For example, at a bank, data includes a customer’s name, the number of accounts that person holds, the amount of money they save or spend, and the transactions they conduct every month. Information is what that data combined can tell you: whether that person is a loan risk, whether they’re about to take their business to another bank, whether they’re a good candidate for a credit card or a better rate.
Analytics is the act of turning data into useful and timely information that is circulating throughout your organization. If all the parts of your information engine are humming–data, technology, people, processes–analyzing and modeling data results in useful and timely information circulating throughout your organization.
If any part of that engine breaks down, you might end up with an Information Gap. You have the data, and you have users waiting for insight. But there are barriers in the middle that prevent data from becoming the information that leads to insight, including:
- Siloed data
- Poorly prepared data
- Lack of communication and collaboration between teams
- Duplicated data, or too many sources of the truth
5 Telltale signs of Information Gaps.
Do you have an Information Gap (or more than one) in your enterprise? If any of these scenarios sound familiar, you may have some gaps to fill:
- There’s a lag time between coming up with a data product and getting it into production. Forty percent of companies say it takes a month or more to deploy a machine learning model into production.*
- Your data engineering team, your data scientists, and your business analysts are all using data from the same applications … from different points in time, in different datasets.
- Your business bases decisions on a statistic or bit of insight. No one has any idea where it came from.
- Information comes straight from the data engineering team into a dashboard, where it gets shared selectively by those who can see it.
- The numbers in that dashboard have zero correlation with what end users are seeing on the front lines.
If any of these things are present in your organization, it’s possible that data is not getting where it needs to be, and not in a format that’s useful for modern analytics. If Information Gaps are present, it’s still possible for your organization to struggle with analytics and accurate insight, even if you have made a move to the cloud.
How to bridge the Gaps
One surefire way to overcome information gaps in your organization is to speed up analytics productivity and provide the entire business with trusted datasets that are shared, secured, and connected. Matillion ETL can help.
To read more about Information Gaps and how cloud-native ELT can help you close them, download our latest ebook, Close the Information Gap: How to Succeed in Cloud Analytics.
footnote:
*“The 2020 state of enterprise machine learning,” Algorithmia, October 2019.
by Tyler Bodys | Aug 16, 2022 | News, WhereScape, WS Doc
The Problem Documenting a Data Warehouse
More data is being collected, stored, and analysed than ever before. One of the digital age challenges is how and where we store all this data safely and accessibly.
A modern Data Warehouse can solve many of these issues, using multi-tiered architecture to ensure different users with various needs can access the information they need. In order to expand and develop a Data Warehouse, documentation is invaluable.
Are you considering approaching a Data Warehouse using a documentation method? Then read on to find out more!
What is Documentation?
Data documentation is vital in many ways for a Data Warehouse, and it’s how you can ensure that your data will be understood and accessible by any user across your organisation. Documentation will explain how your data was created, its context, structure, content, and any data manipulations.
Documentation is crucial if you’re looking to continue developing, expanding, and enhancing your Data Warehouse. However, it’s essential to understand what documentation entails to ensure your Data Warehouse operates smoothly and its processes run smoothly.
Documenting a Data Warehouse
Like we said, the amount of data that we collect as store as organisations is increasing and traditional Data Warehousing that may be set up using a simpler database structure will often struggle to cope. Partially with the sheer volume of information it needs to store and analyse, it also needs to be accessed by various users, often in different ways. A document-based approach to data warehousing will allow for streamlining of data from multiple sources and multi-user access.
When documenting your Data Warehouse, you should begin with creating standards for your documentation, data structure names, and ETL processes, as this creates the foundation upon everything else is built. A robust and excellent Data Warehouse will have straightforward and understandable documentation.
A successful Data Warehouse implementation will often come down to the data solution’s documentation, design, and performance. However, if you can accurately capture the business requirements, then using documentation, you should be able to develop a solution that will meet the needs of all users across an organisation.
At Engaging Data, documenting a Data Warehouse has become second nature. Although it’s not necessarily the easiest or most logistically straightforward part of the process, it’s necessary to ensure your data warehouse processes run smoothly.
What Documentation do I need for a Data Warehouse project?
The exact pieces of documentation that you need may vary by your particular Data Warehousing project. However, these are some of the significant elements of documentation that you should have:
The Business Requirements Document
will outline and define the project scope and top-level objects from the perspective of the management team and project managers.
Functional/information requirements document
which will outline the functions that different users must be able to complete at the end of the project. This document will help you to focus on what the Data Warehouse is being used for and what different pieces of data and information the users will require from the data warehouse.
The fact/qualifier matrix
is a powerful tool that will help the team understand and associate the metrics with what’s outlined in the business requirements document.
A data model
is a visual representation of the data structures held within the Data Warehouse. A data model is a valuable visual aid to ensure that the business’s data, analytical and reporting needs are captured within the project. Plus, data models are helpful for DBAs to create the different data structures to house the data.
A data dictionary
is a comprehensive list of the various data elements found in the data model, their definition, source database name, table name and field name from which the data element was created.
Source to target ETL mapping document
which is a list focusing on the target data structure, plus defines the source of the data and any transformation that the source element goes through before landing in the target table.
What are the problems of Documenting a Data Warehouse?
Documenting a Data Warehouse can be a massive project, depending on the amount of data, the number of users that need access, and the business requirements. As the amount of data held within a Data Warehouse increases, management systems will need to dig further to find and analyse the data. This is especially an issue within traditional Data Warehouses, and as data volume increases, the speed and efficiency of a data warehouse can decrease.
Generally, spending time to understand and document your business needs will make documenting your Data Warehouse easier because Data Warehousing is driven by the data you provide. If you don’t take the time to map these critical pieces of information early in the process, you may run into problems later on. Similarly, the correct processing of your data and structuring it in a way that makes sense for your organisation today and in the future. If you don’t set yourself up for the future, structuring data becomes more complex and can slow down the processing as you add more information to your Data Warehouse. In addition, it can make it more difficult for the system manager to read the data and optimise it for analytics.
Overall, the better the initial documentation, planning, and business information model are, the easier your implementation process will be and make it easier to continue to add data to your warehouse. By carefully designing and configuring your data from the start, you’ll be rewarded with better results.
Another potential problem in documenting a Data Warehouse is choosing the wrong warehouse type for your business needs and resources. Many organisations will allow various departments to access the system, stressing the system and impacting efficiency. By choosing the right type of warehouse for your organisation and making a future-proofed decision, you can balance the usefulness and performance of your data warehouse.
Data Warehousing is an excellent system for keeping up with your business’s various data needs. By making many long-term decisions and preparing at the start, you can avoid many potential problems when documenting your data warehouse. However, you can prevent many challenges associated with data warehouse deployment and implementation by utilising a tool like WS Doc.
What is WS Doc?
WS Doc is a simple-to-use tool that automates a lot of the processes of documenting your data warehouse by automating the publication of WhereScape documentation to your choice of WIKI technology.
In addition, with WS Doc, you can collaborate on workflows, editing data sets and input, allowing various users to work on the project simultaneously. As well as integrating with other apps and systems, WS Doc makes collaboration and streamlined working possible.
Why was WS Doc created?
WS Doc sought to bring document automation and assembly to more industries, turning tedious and detailed work into automated processes and systems.
By allowing you to gather data and instantly generate template documents, even generating document sets from your data, you can save up to 90% of the time that you’d have spent on drafting documentation.
By automating the publication of WhereScape documentation to your choice of WIKI Technology (Confluence, SharePoint, GitHub, or something else), you’re providing your documentation with the power of the WIKI technology, allowing it to be easier to digest, apply, and share.
Overall, WS Doc streamlines and automates the process, speeding it up and making it less resource-heavy.
Want to learn more about WS Doc?
Click the button below. Everyone is on the same page with WS Doc.
Why Choose WS Doc?
In conclusion, by choosing WS Doc to document your Data Warehouse project, you’re utilising a simple tool to automate processes that otherwise would take a long time, as well as using a lot of resources, and that’s not even considering the possibility of human error in a process that requires a lot of detail and repetitive actions.
We’ve discussed some potential problems you can run into when documenting a Data Warehouse. However, with WS Doc you can overcome these issues because WS Docs is a tool that promotes effective communication and collaboration, engaging with the people using data. It saves time and resources by automating the publication and implementation of documentation. And finally, it ultimately enhances your existing toolset, offering a developed, streamlined, and simple-to-use experience.
Here’s at Engaging Data, we use WS Doc in the documentation of Data Warehouse projects we carry out for our clients.
If you’d want to learn more about the process or see if WS Doc could be the right tool for your organisation, schedule a call with us!
by Tyler Bodys | May 30, 2022 | Learn, Matillion, News
Moving to the Cloud
7 Expert Tips for Selecting the Right Strategy and Tools
Moving your data to the cloud can help manage costs and increase agility. In the cloud, you can scale up or down as needed to handle spikes in demand and control costs. The cloud also offers more choices for SaaS applications: Cloud provider marketplaces are filled with tools that can help you move your data, transform it, analyze it, and meet just about any other need you have.
Peter Choe, Data Practice Lead, Ippon Technologies USA and Shawn Johnson, Solution Architect, Matillion, chatted on a webinar about the right strategies and tools for moving to the cloud. Here are some of their top tips for a successful cloud implementation and migration.
1. Build your cloud strategy around people, processes, and technology.
When building a data strategy, it’s important to look closely at your people, processes, and technology. When you understand the existing state of your organization, you can come up with a strategic plan for a future state.
People
When looking at your people, you need to understand the skill sets your employees already have. You don’t necessarily want a strategy that forces everyone to re-skill. Building off your employees’ existing skills will allow you to pivot to the cloud more quickly and better assess which gaps to fill right away and which skills to develop over time.
One great way to identify all your existing skills is to build a RACI chart. Also known as a responsibility assignment matrix, a RACI chart shows the skills, roles and responsibilities of your employees: Whether they need to be Responsible, Accountable, Consulted, or Informed on particular initiatives.
Processes
It’s important to understand how your existing processes intersect so that when you move one thing to the cloud, you understand what else might be impacted. With a thorough understanding of your existing processes, you can create a strategic migration plan.
Technologies
There are numerous technologies that can easily be migrated from on-premises to the cloud. For example, Unix and Linux scripts can easily run on cloud platforms. Also, SQL is used extensively in on-premises solutions as well as in the cloud.
Again, assessing existing technologies can take advantage of skills your employees already have. Do research to determine if you can simply move your existing technologies over or if there’s a better technology option available in the cloud.
2. Get buy-in from your entire organization.
If you want to move your business to the cloud, you need to have buy-in from everyone from low-level developers to C-level executives. Everyone needs to have a clear vision of what the goals of the project are.
When talking to developers, you may want to talk about the day to day: efficiency and ease of management. Data teams are overloaded and increased productivity is an immediate benefit. When talking to C-level executives, you’ll want to focus more on the big picture and business value: future development, speed to analytics, ROI, delivering value to end consumers and the actual dollar value of the project.
3. Start small to achieve quick wins.
When migrating to the cloud, avoid the “Big Bang” approach. Start small. Doing so can help quickly demonstrate the ROI of moving to the cloud. Starting small also helps developers build up their confidence in working with new tools and technologies.
If you start with smaller, more tangible projects that yield immediate business value, you’re more likely to reinforce the importance of a bigger initiative. Also, in your first endeavor, you’ll no doubt run into bumps in the road that require you to correct course. These shifts are easier to make on a smaller project.
4. Determine the right method for moving to the cloud.
There are three common methods for moving to the cloud that the majority of organizations employ for their first cloud migration:
Lift and shift
Lift and shift, or load and transfer, is exactly what it sounds like: You basically move an application and its associated data to the cloud as-is, with no redesign. When you are just beginning to learn about the cloud, lift and shift might be the easiest, fastest, and most cost-effective way (in the short term) to get an existing on-premises application or process moved to the cloud. It’s also a great way to become more familiar with the cloud.
Load, transfer & sync
This is similar to lift and shift, but once you’ve loaded and transferred to the cloud, you then try the different cloud services that are available and swap them out for increased efficiency. For example, you might move your application to the cloud, but swap out the database for a cloud-native database. With this approach, you can benefit from the automated backup and operations that cloud services provide.
Re-architect and re-platform
This approach requires the most time and effort. It involves re-imagining how your application will run on a cloud platform, then re-designing it to take full advantage of cloud-native capabilities.
This method may be useful if your current architecture is unable to scale to meet future business needs. It can also help you achieve cost savings over lift and shift in the long run. However, this method is the most time-consuming and difficult up front.
5. Choose the right cloud provider.
All the major cloud providers offer a variety of managed services and components that you can use to expedite your move to the cloud. Review the marketplaces for each provider to determine if they offer the applications and microservices you need. Some providers also offer a cloud adoption framework to help support your cloud migration plan.
Even as you choose a provider, keep in mind that you may need to make a change at some point in the future or need a multi-cloud strategy. Consider ways to make your applications cloud-agnostic.
6. Use cloud-native data loading and ETL
Whether you are performing data transformation or simply loading data into the cloud, cloud-native ETL products can help you increase productivity and accelerate time to value. Trying to adapt on-premises ETL tools and processes to the cloud won’t take advantage of the platform’s speed and scalability like a cloud-native solution will.
Matillion supports all the major cloud data warehouses, and it provides a graphical, low-code/no-code user interface that can generate SQL for you. Pre-built connectors help you get your data into the cloud from common data sources, and the ability to ‘Create Your Own Connector’ using REST API ensures that you can bring data into the cloud from virtually any source. Data teams are overloaded and Matillion helps them move faster and more efficiently, increasing the speed to analytics.
7. Always be evolving.
The cloud is not a static technology. It’s always changing. Your data journey will continue to evolve as well. Also, your move to the cloud may be gradual, and you may be maintaining some on-premises applications for years. Be prepared to continually evaluate your services in and out of the cloud to improve efficiency and take advantage of new and emerging technologies wherever your data resides.
Learn more about planning a move to the cloud.
To learn more about selecting the right strategy and tools to support your cloud transformation, click here or contact us. We’re happy to provide more links to articles, research, or answer your questions on moving to the cloud.
