Data can be immensely valuable to Australian businesses. However, its value is wasted unless you can figure out how to use the data and apply the insights you’ve gained from it. This is a primary hurdle preventing many organisations from getting started with data analytics. Read on for an overview of how to implement a data analytics program that’s likely to more than pay for itself by improving efficiency and profitability for your organisation:
Assess which data should be collected and analysed
Data is most useful when you can harness its power to solve the perplexing problems your business encounters. Therefore one of the first steps in implementing a solid data analytics strategy is identifying the specific problems that your business hopes to solve using data. Once you’ve determined that, the next step is to decide which data needs to be collected and analysed in order to most effectively solve the problem at hand.
The following are some examples of problems that other businesses are solving using their data:
In the finance industry and retail sectors, executives are using data analytics as one weapon in their multifaceted fight against fraud. Interestingly, fraudulent transactions tend to follow different patterns than successful, legitimate business transactions do – and you’re likely to find that you can reduce losses from fraud simply by analysing these patterns to be aware of what they look like. Once you’ve accomplished that, it becomes fairly easy to flag suspicious transactions for managerial review.
Setting realistic prices for products and services
Pricing is one of the most critical factors affecting the success or failure of a business. It can be a real challenge to price products or services in a way that will be profitable for the company yet offer sufficient value as to be appealing to customers.
In the hospitality sector, AirBnB is using data analytics to help their hosts determine equitable pricing for their property rentals. The algorithm they’ve set up analyses multiple factors including historic demand, local conferences and events, location of the property and amenities available onsite.
Optimising Logistics and Vehicle Maintenance
Forbes reports that UPS is using data analytics to save millions of dollars in maintenance costs for their massive fleet of vehicles. Their strategy involves installing sensors on the vehicles and using the data collected to proactively change out parts when each is at the predicted end of its useful lifespan.
These are just a few examples of how data can be used to help your team navigate the challenges you might be facing. There are many other ways your company’s data could potentially be harnessed and exploited to improve your operations.
Overall, analytics tends to be most successful when you use it to enhance your colleagues’ ability to do their jobs – so keep this goal in mind when you choose which problems to work on solving using data.
If you give some careful thought to your company’s value chain, you’re likely to come up with massive numbers of problems that data wranglers could help you solve. Identify the points in your workflow that could potentially benefit from implementing a data-driven strategy, and prioritise the points that seem most promising.
Train or hire the right team to handle the data analysis
The success of your data program will completely depend on the competence of the people you hire to do the job. Unfortunately, it’s hard to find people who have the right skills to implement a data-driven strategy on behalf of your business.
While it would be ideal to hire a team of people who have already acquired expertise in this field, it isn’t realistic to expect that you’ll easily be able to do so. Even the big tech companies are having a hard time filling their vacant positions for skilled data analysts.
Don’t give up if the perfect candidates don’t magically appear in response to your help wanted ads. The most realistic strategy is to promote talented people from your existing team and train them to do the work at hand.
Select your best coders and problem solvers for the data analytics team. Ideally, these people would already have an undergraduate degree in a subject such as computer science, mathematics or statistics. To give them the expertise they would need to succeed with data analytics, you could incentivise them to obtain additional qualifications – perhaps relevant certifications or a master’s degree in data analytics.
Update your organisation’s data architecture
Unless you’ve recently overhauled your company’s data architecture, it is likely that your team is working with a system that’s less than ideal. You’ll want to update it to accommodate the data pipelines, data lakes and other infrastructure that will be needed for success.
There are some important data architecture principles that it’s prudent to follow:
- The system must be implemented with security as a primary consideration.
- Your business needs to define a clear and specific data governance strategy. The executives in charge of the project must give careful consideration to setting up the system so that people who need access to the data can have it freely but people who shouldn’t have access cannot get it.
- Data should be treated as a shared asset amongst approved stakeholders, and the architecture you implement must allow all relevant parties to have easy access.
Identify successes and failures; then iterate
Data-driven decision making is fraught with complexities to overcome. You should realistically expect to encounter challenges and endure failures on your way to arriving at a workable data analytics strategy. After working through these challenges and failures, successes should hopefully start to emerge.
You’ve already identified a list of problems that could potentially be solved with data analytics; after you’ve tackled the highest priority problem, move on to the others. Iterate on your successes and work at scaling up the data analytics program to maximise efficiency and profitability for your business.
There’s more that can be done beyond these 4 things, but these steps give you the basic outline to follow when you want to implement a successful data analytics strategy on behalf of your organisation.