5 Common Data Preparation 'Missteps'


The vast majority of organizations consider data preparation their biggest bottleneck. It costs billions of dollars and delays discovering insights. But reliable data is a necessity in advanced analytics and without it, analytics solutions results are incomplete, or worse, inaccurate.

When preparing data for advanced analytics, keep these common mistakes in mind so that you can be sure to avoid them.

1. Beginning without having defined the goal

Sometimes enterprises are overly eager to begin their advanced analytics journey, so much so, that they jump in without having defined their goal.

What do you want to learn from its data? What goals do you hope to achieve? Without a clear goal, it's difficult to justify the investment in a data analytics solution. What’s more, while employees are exploring the data and pulling insights, these insights may be of little value to the organization. Finally, if the data being considered is not quality data, you can’t trust that it’s telling you the truth.

2. Overlooking data quality issues

Advanced analytics is only as useful as the data it’s given. Quality data ensures all outcomes are consistent, conforming, complete and current, and as such, companies must ensure every data set they use meets these criteria.

Data cleansing isn’t a one-step process; it’s something that can, and in some cases should, be used across an entire workflow. 

For example, one analysis may reveal unanticipated data flaws, leading employees to source additional data. For departments with multiple data sources, like marketing, separate data sets might need to be combined, or adjusted, before they’re used in a particular analytics tool.

3. Having the wrong people prepare the data

Sometimes companies have their data scientists prepare their data for analysis and modeling, but this shouldn’t be a data scientists’ responsibility. Data scientists are responsible for modeling data and deriving insights, so having your data scientists spend their already precious time preparing data is a waste of talent and money.

4. Neglecting the power of data visualization

Data analysis is much more than just number crunching, thanks to data visualization tools. There are numerous benefits in presenting data visually, and one of the most important is that humans are more adept at processing and understanding information visually, thus visualizations make it easier to spot patterns and anomalies. Similarly, when leadership can see data in a visual way, they can better understand insights, as well as the significance of potential decisions.

5. Data is only a piece of the overall puzzle

Gleaning insights for strategic decision making is one of the great advantages of advanced analytics. But it’s important to remember that insights must always be put in context. Let’s say an analytics solution determines the ideal location for a new distribution center. What the data omits, however, is that this distribution center would likely affect the local ecosystem. Cultural, environmental and ethical issues should always be considered in tandem with insights.

There’s enormous potential for advanced analytics, when done right

Enterprises across industry and geography use advanced analytics for improved and informed strategic decision-making. But as with all things, missteps do occur, and so extreme care should be taken to avoid them.


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