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Guidance and results for the data-rich, yet information-poor.

Introducing advanced analytics can be a complicated undertaking for any enterprise. Adding to the confusion is a wealth of buzz words that can be difficult to explain to most employees, such as business intelligence, big data, the internet of things, and predictive analytics.

The relentless pace of new and emerging tech also adds to the challenge and many businesses find it difficult to figure out how all of this relates to their operations. When enterprises analyze the resources needed — time, skills, hardware, software, and more — any attempt at analytics can be daunting.

Here are a few steps you can take to help lower the risk of failure and significantly raise the chances of succeeding with analytics:

1.) Forget About Choosing A New Tool

More tools and software will just add to your expenses even before you’ve achieved anything. As your analytics program expands, that’s when you should outgrow your present toolset. Take note that many analytics efforts fail not because of a lack of technology, but they fail because of a lack of clear plans, execution, and skill. A new tool won’t help you with inaccurate or unavailable data, so don’t think that it will solve your problems in that area.

2.) Think Small — Find An Easy Win

The problem with beginning with big projects is that they tend to lack a unified vision and direction. Start with a project that’s easy to focus on and solve, and has low costs. Choose an area in your enterprise that you feel suffers from a lack of insight. An ideal situation is when you have a problem that already has complete and accurate data available. Once you solve the issue and are able to present a positive return on investment (ROI), it’s time to present to leadership.

3.) Shift Your Organization’s Thought Process

When you present your successful analytics project, include an ROI calculation as a reporting metric. This helps your group to change how leadership views analytics: from being an added expense, to a source of revenue.

The ideal outcome is that leadership will trust you again and allow you to reinvest into another project. There may be dozens of available tools at the moment — with more and more popping up regularly — but it’s best to stick to the basics. While tools are helpful in discovering and analyzing data, they won’t be able to solve your business’ challenges by itself.

For the past years, advanced analytics has been a staple in the list of many business investments that leadership is interested in, but rarely acts upon. Many top executives are more than eager to reap the rewards of data analytics programs, but they fear the risks and often find it difficult to see how it can directly improve current operations.

When it’s time to present your analytics project to leadership, your group should make it a high priority to thoroughly educate them on the possible benefits of taking on more analytics projects. To help you strengthen your case, include these other topics in your discussion as well:

1.) The Role Data Analytics Will Play In Improving The Organization

When you start an analytics project, determine the value opportunities that analytics can provide once implemented in your business. Many groups get stuck due to wanting to answer all possible questions over how the practice can benefit the organization — but identifying the role that analytics will play in improving the business and explaining how it can help the business move towards organizational goals can be very convincing.

2.) How The Organization Needs To Adjust With Successful Analytics Projects

All organizations need to change how it operates to become more adept at handling analytics and in discovering the possible benefits available from value opportunities. Success cannot be attained if leadership doesn’t completely and thoroughly support the needed changes. It’s best to outline possible adjustments to the business’ policies, skills and processes as early as possible.

3.) How Your Competition Uses Analytics

Two crucial parts of your group’s report are how competing businesses already use advanced analytics and how it has affected their organizations. You should also touch upon how this practice has affected your current business as well, and what you need to do to match–or outpace–their progress. If data analytics hasn’t made a significant impact yet with competition, inform leadership of the possible impact that analytics could have on your position among competitors instead.

One of the common roadblocks to successful model development or process implementation is how most leadership insist on immediate results and return on their investment in analytics. Instead of taking time to design and develop each analytic model, it is an all too familiar scene when management demands implementation in impractical time frames.

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predictive analytics

Ideal results in Predictive Analytics projects are produced when an organization has members that are experts in either analytics or in their own domain. While it’s not necessary—or realistic—to have someone who is fully proficient in both, it can be a definite boon to have basic knowledge across general areas.

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business-problems-solved-by-predictive-analytics

One of the most common questions that get asked about predictive analytics is, “Where do we begin?”. Before anything else – software, techniques, and even training – your business needs to decide on what kind of problem to solve first. What problem will provide the best ROI when predictive analytics is applied to it?

A solid area to focus on is in your organization’s daily operations. This kind of single transaction/interaction with a customer is also called an ‘operational decision’. Wondering what the best offer you could make to a customer in order to keep them or judging whether a claim is fraudulent falls under this type of decision. This is a practical area to begin with because single transactions make it easier to create effective predictive analytic models.

Ever single order, application or claim requires that a decision be made. Because of their simple and straightforward nature, there are fewer challenges to building predictive models based on these transactions. The high volume of transactions means there is a wealth of data available for analysts to work with. More often than not, there will be a significant amount of data for each customer, with every additional transaction contributing more data. Businesses that hope to gain insights on customer behavior over time will then greatly benefit from relying on predictive analytics.

With these transactions, only a set number of actions can be observed, making it easier to link outcomes or results to specific choices. This will then build a defined feedback loop that helps your business improve predictions as time passes.

The sheer volume of orders or transactions finally allows a lot of room for testing and experimentation. For example, it makes it easier to try variations of approaches to cross-selling with different orders to see which approaches are most effective. The resulting data collected also greatly improves the quality of succeeding predictions. Begin your journey to predictive analytics success by working on improving your day-to-day transactions!