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Analytics is a Waste of Time

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  • Senior Analytics Consultant

    I am a result-driven and enthusiastic professional with extensive experience in Business, Financial, Statistical and Data Analysis, in diverse business domains and industries. …

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The 5 Mistakes You Make when You Start with Analytics

When you start with Analytics you might feel overwhelmed, you have all that incredible power in your hands but don’t know how to wield it most effectively.

From my own experience I can tell you that starting with Analytics is not different from learning a new language or how to play a musical instrument, the more you practice, the better you get.

So just how do you go about getting the most from this valuable asset? You can start by avoiding these five common mistakes.

1. Not putting data at the centre of your business

Data isn’t the next big thing anymore. It’s the now big thing, and that means if you’re not harnessing the power of analytics today, you’re already falling behind.

The most successful businesses know this. They’ve put data at the centre of their operations, streaking ahead as leaders and experts in their fields. They’ve transformed and tweaked strategies that re-form their entire business model, to include data-driven decision making at every level.

They know that just having raw data doesn’t mean much, unless you’re able to analyse it properly – and that means interrogating it for real insights you can use to solve complex business challenges.

2. Starting too big

It’s always good to plan for a big, bright future, but it’s never good to get too far ahead of yourself. We see lots of businesses overestimate their analytical maturity and readiness – with disastrous results.

Here’s how you can score some easy wins, and get valuable experience on the board in the process:

  • Identify and prioritise business cases based on the value, impact, and your organisation’s readiness. Remember, you’re trying to find the sweet spot where all three come together.
    • Focus on quick-win business cases as your first deployments. They can create a nice, big ROI that comes fast enough to prove the analytical benefits to the rest of the company.

Find fast, easy solutions that provide real answers to real problems for business users, your employees, and your customers.

3. Not setting up an analytical environment and infrastructure

You need to set up the kind of analytical environment and solid infrastructure that gives you the best chance of success. After all, data analysis doesn’t happen in a vacuum.

To get the background support you’ll need for your plans, you’ll need to form an Innovation Program that includes both Technology and Human Resources.

For the Technology arm, there are a few things to consider:

  • You’ll need to consider all your deployment options. Will you choose a hosted solution, or is the cloud better for your business? Is it more efficient to host on your premises? The answers will depend on your organisation and industry.
    • Make sure you identify any software and hardware requirements for your upcoming changes. That means preparing a long-term budget so you’re able to scale when you need to.
  • Every solution should consider the main integration requirements. As analytics requires access to different sources of data, and reveals that information to various systems and operational channels, those channels and systems need to be customised to host the relevant information.

For HR, you’ll want to include resources for a range of people in your organisation:

  • C-level personnel, because it’s the best way to get executives and their peers interested and talking.
  • Data owners, who will need to understand their role in the new systems. More importantly, they’re the people who know the data you already have intimately, as well as the kind of data you’ll need in future.
  • The same goes for data scientists and analysts in your organisation. It’s important to have these people onside from the start.
  • Don’t neglect business users, especially when they might not have full visibility into decisions you’ve made regarding the new business direction. Keep them informed as much as you can. After all, if they buy-in, they’ll be more inclined to share business expertise and knowledge with your data scientists/analysts.

4. Treating Analytics as an enemy

It’s vital that your new analytical outlook comes with training and clear communication, so you’re not leaving people adrift in the middle of the field. Help them embrace analytics and they’ll be valuable allies when it comes to transforming the way you do business.

Don’t forget C-level resources that align them with the analytical vision as well; you’ll want as much support as possible at every level.

In the rush to create change, make sure to include employees who might feel threatened or intimated by external consultants.

You know those consultants are vital for helping you take your first steps, but that might not be as obvious to others.

So talk. Make it clear the consultants aren’t there to replace their skills or experience; they’re there to help their careers evolve and progress. Let them know that learning to use analytics can actually help them become more efficient, save time and test their perceptions.

It’s even possible to set up a reward system to entice people to shift their careers appropriately. Today’s traditional business intelligence experts could be tomorrow’s data scientists.

5. Not focusing enough on Deployment

When analytics fail it’s usually not because of a lack of insights, but a failure in deploying them into your business environment.

Yes, at its heart analytics is all about modelling, but when you’re taking your first steps you can’t expect to see perfect accuracy. Any results – however poor – will always be better than a random guess.

So, take a deep breath and remember:

  • Analytics is not a silver bullet for every challenge. It’s not a case of having a one-size-fits-all solution: every solution will need to be tailored specifically to your business challenges
  • The more you learn from your data, the better results will improve. Analytics is a learning process, full of fine-tuning, testing, monitoring, and managing – over and over again. There’s no short cut to success.
  • Focus on insights. Analytics isn’t just for telling you the probability of an event. It’s a holistic science that delivers information, exposes patterns, and uncovers causes and effects throughout your organisation. Make sure you’re making the most of it.
  • Understand how data can be used to enhance and improve existing processes and performance. Armed with valuable insights, you have the power to improve and finesse existing processes and performances.
  • Sharing results creates feedback loops that will enhance your processes. You can then feed these insights back into the business to optimise and streamline processes across the organisation.

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