Thank you to Elizabeth for help in writing the article, and thank you also to Pete for the inspiration! (You know who you are!).
Those of us who work with data every day can forget that business intelligence and data science can be complicated and doesn’t immediately make sense to everyone. Industry experience tells us that it is much easier to get a Business Intelligence or Data Science project off to a good start than it is to keep up the momentum as your project progresses.
We all know the Grand old Duke of York story. He had a vast team who went halfway up the hill, and they were neither up nor down; they did not know where they were going. Business Intelligence, and more generally, Data science are tricky subjects and businesses can often feel like they are moving towards progress and then have to take a step backwards to solve an issue.
An initial foray into Power BI can be fun, but then it starts to get hard. Understanding data isn’t easy, and finding stories in your data takes practice. But because the initial steps of a project can seem easy enough, many of us will feel discouraged when reality hits us further down the line. This issue is the ‘Grand Old Duke of York’ problem being something of an issue in Business Intelligence — it’s too easy to lose motivation and give up halfway through a project.
Maybe the enormity of the data makes individuals begin to feel overwhelmed. Perhaps the software confuses you, or the data is too disorganized. Conceivably the project is insurmountable, with too many steps and not enough time. But there are several approaches you can take that will help you to get over the stumbling blocks, and make your Business Intelligence or Data Science project feel a lot more achievable. Power BI can help you to get back over the hill with your data, and to keep moving towards success. This article will outline some of the common problems people have that trip them up and will suggest a few approaches that could help.
Problem: I don’t understand BI systems!
Maybe you’re not entirely up to speed with the software you’re using, so any of the more detailed analyses or calculations you attempt after the initial steps throw you off. BI software is not always intuitive, and there is a learning curve involved, but it’s easier to learn how to use BI than it is to learn how to interpret data without it. It is possible to put a training program in place or a designated colleague (or colleagues) who is confident with the software and can be available to provide support. Peer-to-peer learning has a high success rate, so having individuals in a team who are happy to show their colleagues how they utilize and report data is a great idea. Having something like this in place can help empower everyone in a workplace to handle BI systems and can be very useful for many reasons.
Businesses should also make sure that they are making their BI systems as accessible as possible to everyone. Building a simple dashboard or creating an entire report can be easy. Showing the most critical data in visualization is something that anyone can learn to do, but more people will be happy to approach it with an open mind if it appears as easy as it is.
Problem: My data is too disorganized. I don’t know what I’m looking at or where to find anything!
If a business doesn’t have a good data organization strategy in place, or has only implemented one recently and perhaps hasn’t quite got it perfected yet, then starting a project that involves any data analysis can feel almost impossible at points. Data organization is such a significant task that, unfortunately, by the time a project is underway, the idea of simultaneously organizing all the data too is pretty unrealistic. Having vast sets of data that are wholly unstructured and disorganized can have serious adverse effects on a business’ productivity; it’s much harder to make informed, data-driven decisions when you don’t know if you even have all the data or the correct data.
Organize data before a project is underway. It’s hard not to lose steam otherwise. Data organization strategies should be put in place regardless of the amount or type of data involved, and there should ideally be a role in the business dedicated to this. Furthermore, the organized data should be stored and presented in a way that it’s accessible to other colleagues — easy to use, easy to work with, and easy to plug into a BI system for analysis.
I don’t know what the point is of me even doing this!
Data is a complicated thing. Sometimes you may not even be sure exactly why you’re doing the project you’re doing. It might be hard to visualize exactly where, how, or why the decisions that your project will drive are going to fit into the wider business.
Businesses need to have a data strategy. According to Dataversity, a data strategy is “a set of choices and decisions that, together, chart a high-level course of action to achieve high-level goals. The organization should include business plans to use the information to competitive advantage and support enterprise goals”. It’s helpful for both the person doing the project, as well as everyone else if it’s easy to understand how and why the data you’re working with will influence the decisions the business makes in the future.
A good data strategy will also include easily-understandable success metrics and both short and long-term objectives — both things that can help do a project, and the broader use of data in the business, easier to break down and understand. In general, it’s helpful to know how what you’re doing fits into your business’s guiding principles and values.
Organizations without a data strategy suffer from a range of issues. They miss out on meaningful information to make decisions, which can range from short-term operational decisions to long-term strategic decisions. Power BI can help with strategy formation and development by exposing the data required, since understanding the data is essential to getting the business model right.
To build a business strategy, founders and leaders need to understand what they want to achieve, and they also need to know the existing terrain of the organization. Business intelligence can enhance and extend the enterprise by supporting its’ decision-making ability. It can have a direct impact on the overall performance of the organization by promoting a cycle of continuous innovation, along with better decision making. It is vital in today’s fast forward and demanding environment to have an overall vision. The vision can help an organization to move from being an imitator to an initiator – an organization that gets copied, not one that imitates other organizations.
Data strategy is a solution that underpins corporate information management across the enterprise. It is not a specific type of technology, such as Excel or Power BI. It is not merely having a set of dashboards. The ‘shiny, shiny’ attitude towards technology should not distract away from the business value that Business Intelligence can provide to an organization. Having Business Intelligence technology in itself does not add value to an organization; it is the use of Business Intelligence that drives the organization forward. “Business users have lost confidence in the ability of [IT] to deliver the information they need to make decisions.” (Gartner, 2009) .Moving forward without a strategy or a roadmap is like having a beautiful plane, but not having a direction to fly in.
Problem: There’s too much to do! I’ll never finish on time!
This problem is a part of data science and BI projects, and any project generally! It’s easy to feel like a big project will never end, and you’ll never meet the deadline. The best solutions for this are to break your project down into small, achievable goals, and to stay flexible and agile in how you manage your work and your time.
Write down a list of steps you need to take, and then write down the steps involved in taking those steps. Allocate time to each task, and try to see your project as a series of smaller individual projects.
If you’ve seen Frozen 2, you may know that there is a common theme throughout: do the next right thing. Start with what’s in front of you and focus on completing it. Then do the same with the next task. Don’t fret if something goes wrong, or there are flaws in the data or results you didn’t see coming — there are always ways around it. Move with your project rather than forcing it to move the way you want it to. Sometimes results may come up that are even better than what you anticipated! And always remember, there’s no shame in asking for help.
If you need help to devise a data strategy, or to have a check for your data strategy, then please get in touch.