This article originally appears on the PT2.0 website and can be found here: PT2.0

Some compare data to oil, with the ability to profit off an unrefined resource. Personally, I think data is more like water.

Firstly, it is an essential life-giving resource, but like water, sometimes there is too much and it becomes run-off, or worse, a flood where you spend too much time trying to keep it at bay and it never gets utilised. Then at other times it is like you are in drought, no matter where you look there is no data fit for drinking or irrigation. This is not a new problem, we have been using data for thousands of years, so why is it being compared to oil?

It is because we now have tools and systems that are capable of not only capturing the data but analysing and purifying it for consumption far quicker. This speed and convenience has started to turn the tables, not that the concept of data is new and now more readily available in a better form. When I started my career the tools that we used to capture, clean, and analyse data cost hundreds of thousands of dollars, now these tools can be acquired for the same price as a few cups of coffee.

The real question for 2022 is, how do you want to use your data this year and for what end goal?

Most reading this have more data accessible then you will ever know and could spend a lifetime trying to consume. For most of us we are in a flood and have yet to start sandbagging!

Want to find out what your competitors are doing? Use Facebook and Twitter to monitor their feeds, use Google alerts to trigger when your competitors do anything newsworthy, but to what end?

My suggestion is for everyone to kick off their 2022 by building a data strategy covering these key points:

1. What do I want to measure to grow my business and why?
2. What decisions will it enable us to make? (we want useful data for decision support, not to just collect it like a bower bird)
3. Where is the data to be able to do this and how do I access it?
4. Do I need to access this data frequently or over time?

So, where to begin?

The easiest way to start analysing most data is to use a program most of you already have…Excel.

Excel is not just for numbers, it has a whole host of tools to start collating and using your data to make decisions (and there is a whole host of training material available for free at microsoft.com.

Of course, there are other alternatives available, like using your accountant or organisations like PT 2.0 who will do the analysis for you including helping with the data strategy. You probably already have the tools you need to get started and there are also more advanced low-cost tools like Power BI available if you would like to automate some of this analysis. From here, you simply load the data, convert into tables or pivots and start being curious! If you started by defining the problems you want to solve, look at your data through that lens.

Here are some examples of data being used in business to help kick-start that thinking.

All Industries:

• What individual products or services (or groups of) are making you money and which are costing you money?
• Which activities are creating value, and which are costing you?
• How has customer buying behaviour changed?What has been the effect on your product/service mix?

Retail:

How is your staff utilisation per day? We have clients starting to measure utilisation per hour and making better staffing and rostering decisions like starting people later, changing the mix of skills and so on.

Manufacturing:

1. With supply chains stretched, what is your new re-order point for hard-to-get materials, what effect does a single day delay have on your process and cashflow?
2. Where are your current bottlenecks in either materials or process?
3. Mapping your rework and failure trends – is it time to invest or retrain?

Professional Services:

1. What is the effect of picking up and putting down work?
2. Are people working at the correct level – is the process optimised?

Finally, whatever is important to your business to measure, look at the data over time and not only a point in time. Analysing data at a single point in time may cause you to miss the larger trend that is emerging (a can’t see the forest for the trees kind of example).

Here is to a great 2022 and happy data wrangling!