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 gained from it. This is a primary hurdle that prevents many organizations from getting started with data analytics. Read on for an overview of how to implement a data analytics program that is likely to more than pay for itself by improving efficiency and profitability for your organization:

Assess what data to collect and analyze

Data is most useful when you can use its power to solve the perplexing problems your business faces. That’s why one of the first steps in implementing a solid data analytics strategy is to identify the specific problems your business hopes to solve using data. Once you’ve established that, the next step is to decide what data to collect and analyze to solve the problem in the most effective way.

Here are some examples of problems other companies are solving with their data:

Preventing Fraud

In finance and retail, executives are using data analytics as one weapon in their multifaceted fight against fraud. Interestingly, fraudulent transactions often follow different patterns than successful, legitimate business transactions – and you’ll likely find that you can reduce fraud losses simply by analyzing these patterns to know what they look like. Once you’ve accomplished that, it becomes quite easy to flag suspicious transactions for management review.

Set realistic prices for products and services

Pricing is one of the most critical factors influencing the success or failure of a business. It can be challenging to price products or services in a way that is profitable for the business while still offering enough value to be attractive to customers.

in the hospitality industry, AirBnB uses data analytics to assist their hosts in setting fair prices for their properties. The algorithm they set up analyzes multiple factors including historical demand, local conferences and events, location of the property and amenities available on site.

Optimization of logistics and vehicle maintenance

Forbes Reports that UPS uses data analytics to save millions of dollars in maintenance costs for their massive fleet. Their strategy includes installing sensors on the vehicles and using the collected data to proactively replace parts when each is at the predicted end of its useful life.

These are just a few examples of how data can be used to help your team address the challenges you may face. There are many other ways your company’s data can be exploited and misused to improve your operations.

In general, analytics is most successful when you use it to increase your colleagues’ ability to do their jobs – so keep this goal in mind when choosing which problems to solve using data.

If you think carefully about your company’s value chain, you’re likely to come up with a myriad of problems that data wranglers can help you solve. Identify the points in your workflow that could potentially benefit from implementing a data-driven strategy and prioritize the points that seem most promising.

Train or hire the right team to perform the data analysis

The success of your data program depends entirely on the competence of the people you hire to do the work. Unfortunately, it’s difficult to find people who have the right skills to implement a data-driven strategy on behalf of your company.

While it would be ideal to hire a team of people who have already gained expertise in this area, it is not realistic to expect that you will be able to do this easily. Even the big tech companies are struggling to fill their skilled data analyst positions.

Don’t give up if the perfect candidates don’t magically appear in response to your help-requested ads. The most realistic strategy is to promote talented people from your existing team and train them to do the job.

Select your best coders and troubleshooters for the data analytics team. Ideally, these people would already have a bachelor’s degree in a subject such as computer science, mathematics or statistics. To give them the expertise they need to succeed with data analytics, you could incentivize them to gain additional qualifications – perhaps relevant certifications or a master’s degree in data analysis

Update your organization’s data architecture

Unless you’ve recently overhauled your company’s data architecture, your team is probably working with a system that isn’t ideal. you will want update it to accommodate the data pipelines, data lakes, and other infrastructure necessary for success.

There are some important principles of data architecture that it is wise to follow:

  • The system should be implemented with security as the primary consideration.
  • Your company must define a clear and specific data management strategy. The executives responsible for the project should think carefully about setting up the system so that people who need access to the data can freely access it, but people who should not have access cannot access it.
  • Data should be treated as a shared asset between approved stakeholders, and the architecture you implement should provide easy access for all relevant parties.

Identify successes and failures; then repeat

Data-driven decision-making is fraught with complexities that must be overcome. You should realistically expect to encounter challenges and endure failures on your way to a workable data analytics strategy. After going through these challenges and failures, hopefully successes should come.

You have already identified a list of issues that could potentially be solved with data analysis; after you’ve addressed the top priority issue, move on to the other one. Repeat your successes and work to scale the data analytics program to maximize efficiency and profitability for your business.

There is more that can be done than these 4 things, but these steps will give you the basic overview to follow when implementing a successful data analytics strategy on behalf of your organization.

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