5 Obstacles Companies Face When Adopting a New Analytics Strategy

Analytics is a general term for a number of different processes, all of which are geared toward better understanding your demographics (or industry in general), and making improvements to your business’s infrastructure to account for those new insights. Unfortunately, whenever a company starts analyzing data in a new way, or from a new angle, there are vulnerabilities that make themselves evident. Without compensating for these vulnerabilities, it’s possible to damage your business more than you end up helping it.

Data and Analytics

Big data is an exciting new possibility for companies, and technology makes more data available to us every day. Data is useful and sought after because it is simple, raw, and objective—but that doesn’t mean it’s always used in an objective way. The only way to gather meaningful information from raw data is through the process of analytics, which is subjective by virtue of the fact that a human being (or team of humans) is responsible for data’s interpretation.

Major Obstacles in Analytics Adoption

No matter what type of analytics strategy you’re adopting, watch out for these possible hurdles:

1. Relying only on analytics to make decisions. Analytics strategies are useful tools to aid you in your decision making, but that doesn’t mean you should only rely on your interpretation of direct data to make a decision. For example, data may not be able to pinpoint red flags that an experienced professional may catch by instinct. It also can’t open the door for creative problem solving; it may suggest a certain problem area, but it often takes a human creative element to find an alternative solution.

2. Getting the entire team on board. If you want your analytics strategy to be successful, you need your entire team working together on it. If you have only one person gathering, interpreting, and acting on your data, you’ll end up with a narrow range of interpretation and a more subjective filter through which your data is implemented. Furthermore, having more team members trained in analytics will help you make more objective, informed decisions across the board.

3. Gathering data from the right sources. Not all data is created equal; some sources are more objective, some are more subjective, and some are unreliable in their measurements. Similarly, some sources may provide you with a massive firehose of data, while others may provide a narrower stream. If you opt for the former, you may find yourself overwhelmed by the volume of data. If you opt for the latter, you’ll face the possible problem of not getting the data you need.

4. Asking the right questions. No matter how good your data stream is, you’re still going to be limited by your ability to ask the right questions of that data. If you get answers to irrelevant questions, you may spend too much time on problems that don’t need to be solved. If you ask a question in the wrong way, you may end up with data that points you in the wrong direction or leads you to an inaccurate conclusion.

5. Following up in the long term. Analytics is never a one-step system; you’ll need to have a good follow-up strategy to ensure that your work is being done efficiently and effectively. For example, let’s say data points you toward a new marketing strategy. Is there any guarantee you’ll implement this strategy effectively? Is there any guarantee this is the “best” possible strategy? Continue to follow up with new measurements and questions to gradually perfect your approach.

Even though data makes it increasingly possible to gather and utilize objective information, we’re still relying on the biases and weaknesses of human-based analytics to find and apply these data correctly. Until our systems become sophisticated enough to recognize these patterns and put their conclusions into action, the only way for businesses to implement new analytics strategies successfully is to compensate for these drawbacks.

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