This blog is one of an eight-part series of blogs – read our introduction to see how this blog fits into the series.
To report on data-driven business trends, many businesses are messing around with hundreds of spreadsheets within their organisations – these are usually created manually, entirely standalone, disconnected from other spreadsheets, and out-of-date before they’re finished. We’ve seen businesses consume as much resource as the equivalent of one full-time employee, through to the equivalent of 30+ full-time employees, just on basic reporting…purely because the processes are manual, time-consuming, and prone to error. Yet most of these reporting processes can be entirely automated end-to-end, giving instant access to data, saving huge amounts of time, and reducing errors.
What do we mean by data outputs?
Data outputs is a deliberately vague term we use to mean all final outputs derived from data, which could include a wide variety of formats, such as:
- Dashboards – a visual representation of the data to show the key metrics
- Data visualisations – graphs and charts used to share the raw data
- Data analytics – analysis of the raw data to derive more advanced patterns and trends
- Data science – application of statistical models, machine learning or artificial intelligence techniques to identify deeper patterns and trends
- Reports – a formal update provided to the target audience in a prescribed format
- Insight – a form of data storytelling to share insight derived from the raw data
- Infographic – another form of data storytelling to convey a message in a one-page cartoon/graphical format
- Logs – a list or tabular format of raw data
This list isn’t exhaustive and some of these overlap – for instance, a dashboard could contain data visualisations, data analytics, data science and logs, and could be described as a report. It doesn’t really matter what you call these data outputs, so long as you’re consistent within your own organisation.
Typically, we’d split data outputs into two broad categories:
- Reports – these tend to be:
- Focused on facts and explaining what happened in the past
- Descriptive and historical
- Delivering trend analysis by year/region/product
- Analytics – these tend to be:
- Focused on insights and explaining why things happened
- Delivering advanced analysis such as forecasting or benchmarking
- Potentially including data science techniques too
Again, the division between these categories is arbitrary, so don’t feel constrained by them – the most important thing is creating a data output that enables your target audience to do their job better/quicker/easier than they do today.
Who are the audiences for Data Outputs?
There tend to be two distinct audiences for any data output – either:
- Internal users – these are in-house teams and staff:
- Tend to be split by job function or department
- Tend to use the data outputs for tracking performance
- Data outputs can be contained within the organisation’s platforms such as stored on shared drives or access via a single-sign-on platform
- Users can be trained to use the data outputs on a self-serve basis
- External users – these are customers, stakeholders, and suppliers:
- Data outputs tend to be used for sharing information with clients
- Data outputs can be contained within a client portal, or as a separate data output emailed to a client
- Users are harder to train/engage/support, as they are not under the direct control of a business, so these outputs need to be more intuitive and well tested
In either case, we need to understand the business requirements of each user, so we can design the data output specifically for them.
There are so many tools available to visualise and share data, and they fit into three broad categories:
- Spreadsheets – whether Excel or Google sheets, every business uses spreadsheets to create metrics and graphs. Spreadsheets are fantastic tools for one-off data analysis, but the entire process is manual, so they are too resource-intensive, time-consuming and prone to manual error, to work successfully for regular reports or analysis.
- Business Intelligence tools (BI) – whether Power BI, Tableau, Google Data Studio, Qlik, Looker or any of the other tools available in the market…these tools automate data analysis and visualisation to create on-demand automated reports. It’s not worth the effort to use a tool like this for a one-off report though, and each tool has a licence fee, but they save huge amounts of time for the creation of automated regular reports and analysis.
- Websites and apps – another option is to build bespoke reports and analysis within your own website/app so you have complete control of the user experience. This approach requires the most time/effort/cost upfront but removes the need for any third-party licence fees in future.
Which one is right for your business will depend on a few things like:
- Volume of users – how many people will need to view the reports? This will influence the licence fees for third-party BI tools
- Scope of data visualisations – how many graphs are required? The BI tools will be the quickest way to build a high volume of graphs…bespoke web/app development will take the longest time
- Frequency of updates – how often will the reports need to be updated? If frequent, the BI tool will provide the quickest, cheapest solution to regularly update the reports
- Ability to self-serve – if you want team members to be able to create their own data outputs, the website/app solution wouldn’t work as it would require coding knowledge, so the BI tool and spreadsheet options would work far better
Selecting the right tool for data outputs needs to be directly related to the business requirements.
Now, you’re ready to go to #4 Tech Stack Options
Now that you understand the business requirements, data sources, and data outputs, you have completed the Discovery phase, and now need to move onto the Design phase, starting with the technology stack options. So, check out our next blog in this series for some simple tips on how to compare, contrast and select the right technology stack for your business.
Well, you’re in the right place. We can run the Discovery & Design programme for your business. The benefits of outsourcing to us are:
- OBJECTIVITY – we bring a fresh pair of eyes to your business and we’re unhindered by office politics, historical decisions, and legacy systems
- INDEPENDENCE – we’re technology-agnostic, so we can give you an independent view, with no vested interest in you selecting, or staying with, a certain vendor, tool, or platform
- AWARD-WINNING DATA CONSULTANTS – we’ve done this before…for 75+ projects and for 50+ businesses, so we can bring our wider experience to the mix
When we run a Discovery & Design programme for one of our clients, it typically takes 4 weeks, depending on the scope of the project. Most businesses want results quickly and simply…so that’s what we do – we worry about the complexity, so you don’t have to.
Find out more at https://data-cubed.co.uk/services/.