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.
Data automation is fast replacing conventional methods for building and maintaining various centralised data repositories, which organisations use to deliver data-driven value and insights.
Businesses can generate vast amounts of data taking on many forms. Structured and unstructured data from CRMs, HR systems, social media and websites – these varying forms and sources of data need to be stored somewhere – this is where Data Automation can help you!
Start accelerating business success automatically with Data Automation.
What is Data Automation?
Traditionally, data pipelines to move or transform data have been manually scripted by developers. not having a development standard, these processes allowed for developers to use their own coding style to apply different methodologies, either to an individual pipeline or a set of pipelines. This made amending code or supporting issues difficult to read through, interpret or alter. This problem was then scaled significantly by the number of developers in a team.
In addition, this can lead to poor process performance where the technology is not used to its fullest extent – or cannot process everything demanded.
Yet, Data Automation eliminates these problems – making them a distant memory after the implementation of Data Automation. Data Automation is collecting, processing and presenting data using automated tools instead of performing these tasks manually. Manually updating data presents a risk of being delayed and creates an additional workload for your team to complete alongside their day-to-day responsibilities.
Replacing manually coded scripts with a low-code or no-code tool helps your team focus on processing and modelling the data without spending hours writing code. With minimal human intervention, Data Automation will collect, transform, store and analyse data using well-designed methods, software and, where applicable, Artificial Intelligence. Eliminating the reliance on manual labour with bots that will do the job for you.
In addition, code standards can be applied to the data processing scripts, creating a consistent and easy-to-support data platform. Gone are the days of flawed, manually coded scripts.
Data Automation tools automate three key elements of Data Automation. This is known as ETL or ELT, depending on what tool you are using:
Extract – Data is exported from source systems like social media, emails, SQL Servers, etc. Into a staging area. This data could consist of various types and be pulled from most structured or unstructured sources.
Transform – Changing the data to fit the purpose. For example, transforming the data into a Star Schema ready for further analysis by Visualisation or Business Intelligence tools.
Load – The data is moved from the staging area into storage. For example, a Data Warehouse or a Data Lake.
What Tools are Available for Data Automation?
With a plethora of Data Automation tools in the market, there is a significant amount to choose from; some are more efficient than others. Some do a good job, and some do an excellent job.
At Engaging Data, to be relevant to the needs of our customers, we only partner with specifically selected Data and Information partners. This approach helps our clients bring true value in realising and delivering their data-led business change journey. Our Partners include:
Microsoft – Take care of what’s important. Automate the rest. Do more with less by streamlining repetitive tasks and business processes – increasing efficiency and reducing cost.
WhereScape – Accelerate with Data Automation. Rapidly deliver a meaningful and future-proofed data platform.
Matillion – Unlock new levels of productivity and get data business-ready faster.
Snowflake – Join the cloud. Execute your most critical workloads in a fully managed platform that capitalises on the near-infinite resources of the cloud.
VaultSpeed – Delivering unparalleled automation of upfront data warehouse design and development. Fully compliant with Data Vault 2.0.
Why You Need Data Automation.
As previously mentioned, Data Automation helps improve productivity around the use of data within your organisation. Some other benefits influence improved Data Quality, simpler Data Cleaning and optimized Data Transformation.
Improved Data Quality –
As a result of datasets being standardized in accordance with formats and schemas, you can use Data Automation not detect and fix any records that don’t adhere to the format. Data Automation helps you improve data integrity and quickly detect errors.
Simpler Data Cleaning –
Data Cleaning transforms raw data, usually unformatted and unstandardised, into a significantly more suitable format for data analysis. Generally, the steps taken in Data Cleaning are repeatable, thus frameworks and standards for future datasets.
Automating these steps, using scripts and schedules to make new data match existing schemas and running steps on a recurring basis whenever new data is needed or imported.
Optimized Data Transformation –
The process of changing datasets from one format to another, Data transformation involves checking the datasets to ensure its in the correct format in the destination repository. Ensure that the data quality is good and checking the dataset follows the set standards for your organisation’s requirements or the data’s destination.
By automating Data transformation, it will help accelerate processes as you will need to manipulate, transform or analyse the data, and it is already in a standardized format.
Not only does Data Automation grant the large benefits discussed above, but Data Automation also allows for these general business benefits:
Enables Better Decision-Making – Businesses can react rapidly to the data’s narrative. Allowing for decisions to be made quickly with more data-led insights
Reduced Risk of Human Error – Manual data entry is prone to human errors that can cause Data Quality issues and affect reporting accuracy.
Improved Operation Efficiency – By setting up the rules and processes with software, you can easily audit and redefine these processes. You can quickly identify any broken or unnecessary workflows.
More Efficient Workforce – Teams no longer need to spend their precious time and advanced skillsets on mundane admin. Instead, they can add value elsewhere as their skill sets are appropriately optimized.
Reduces Operational Costs – You can get substantially more done with the same resources: minimal investment whilst maximising return.
Scalability – Automation software is ready to scale with your business, so you can utilise its benefits regardless of where your business is now and how much it grows in the future.
How Engaging Data can help You with Data Automation.
At Engaging Data, we understand that everyone needs data built efficiently to gain value quickly.
Using innovative Data Automation tools, we help you seamlessly integrate your data into accessible and secure platforms. Building data for a purpose, we only process relevant information to achieve your goals.
Do more with less effort.
Let us help you enable your teams to do more, eliminate human error and produce a higher standard of work with minimum effort.
Automate your data now.
Fill out the form below, and work with the experts behind Data Automation.
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.
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.
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?
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.
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.
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 at Engaging Data, we utilise WS Doc in the documentation of Data Warehouse projects we carry out for our clients.
If you’d like to know more about the process or see if WS Doc could be the right tool for your organisation and data needs, why not fill out the form below, and a member of our expert team will be in touch?
