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TDWI Upside - Where Data Means Business

Forget Big Data -- Find the Right Data

If we aren't doing anything useful with big data, are we falling behind?

Although big data has become prevalent in many industries, many executives still worry if they should be adopting big data (or at least considering it) for their enterprise. The number of technologies available to tackle big data challenges has skyrocketed in the last couple of years, but enterprises still don't understand where big data analysis would actually be useful.

In many cases, big data isn't actually needed. It's time for enterprises to look beyond the potential use cases of big data analysis and dig deeper into their core business to find the use cases of the right data.

The Right Data

What is this right data and how do you find it?

Right data is the most important data in an organization; you must understand it deeply and act on it to maximize profits, reduce costs, and run your operations smoothly. The right data is not limited to transactions that affect revenue or expenses. Instead, think of it as the data that drives your organization to work together and act on it collaboratively.

The right data differs by company, depending on what your competitive business priorities are. Even so, there are three common characteristics of right data no matter what it is. Understand these characteristics and you'll know where to find the right data in your own enterprise.

Characteristic #1: Right data drives action

This is the most important characteristic of the right data. Analyze your business, your critical decisions, and the potential turning points in your enterprise. Consider what possible actions you can take. Determine where you spend the most time thinking about your decisions. What information is driving key decisions and actions?

Right data is not what you see in a report that's viewing things in hindsight. Right data also isn't simply a list of key performance indicators (KPIs). Rather, it's what drives action in your enterprise.

For example, in an enterprise software firm, license renewal revenue drives action because it's the biggest and easiest revenue component to measure. It's also what matters most to the organization's bottom line. The data enabling the right actions to improve the bottom line is the right data. I worked with a software giant where license renewals were the primary action driver and a big priority for the organization, but there were several process and data issues that prohibited accuracy and timeliness. This is a perfect example of the right data the enterprise should focus on.

Characteristic #2: Right data feeds off collaboration

The right data is used, discussed, and acted on by several departments or groups within an organization. That's why the right data drives and, in turn, feeds off of collaboration across all of those groups. All organizations have data that several groups access or share, but right data is also discussed, manipulated, and acted on by those groups.

Spend time on the actions and decisions that require major collaboration across your organization and you will find the right data right there. The right data makes collaborative actions easier. For example, sales forecasting and inventory planning is a collaborative process at any manufacturer. Employees in marketing, sales, supply chain, and finance all must be involved in creating or managing the right data for sales forecasting and inventory control. If any group is overlooked, the results are often detrimental to the organization as a whole.

For example, if sales and marketing are involved with the right data but supply chain hasn't vetted and approved the forecasting and inventory data at a detailed level, then the organization might be over- or underestimating production capacity and setting the wrong customer expectations. On the other hand, if supply chain and finance are involved with marketing but sales is not involved in setting forecasting or inventory data, the result might be a change in the inventory that won't address their customers' needs or timing. In addition, the enterprise will have to deal with the wrong inventory levels internally.

That's why it's so important to focus on the right data. Right data is ripe for money-making analytics.

Characteristic #3: Right data is relevant and timely

The final characteristic for right data is its relevance and timeliness to the organization. An organization should always be focused on current operations, of course, but also on its future vision. Data sets that answer questions about current operations and the future are the basis of your right data.

Data that simply looks back is not relevant. Data that looks back in order to gain a better understanding of the past, so that the future can be changed, is the right data. This is because it can change today's actions and tomorrow's plans. This also implies that the right data is not static; it keeps changing as the organization changes, matures, and reprioritizes for the future. The right data is relevant to the organization's problems now and enables the organization to think and plan ahead with accuracy.

For example, for a young company launching its consumer product, consumer adoption and usage data is the right data to analyze and take action on. However, as the company matures into multiple product lines and different usage patterns, understanding retention by product and also margin by product becomes key because the organization needs to prioritize resources and spending across multiple products. The products that perform well today will command an organization's attention, but so will the products that show great promise for the future.

Right data tied to the right analytics framework creates money-making analytics -- those analytics that help companies focus on top-line growth and bottom-line impact. (To read more about money making analytics and how to create them, read this series.)

Notice how none of these conditions has anything to do with how big or small the data is, what technology you are using (or not using), nor whether you are using open source or cloud or something else. This is about understanding your business and your business priorities in the competitive landscape. Once you understand the right data and the associated money-making analytics, you can add huge value to your core business.

About the Author

Shikha Verma is senior vice president at Diyotta, a data integration company. She is a data and analytics strategist, leader, and advisor to several companies in Silicon Valley. In her career, Verma has helped several Fortune 500 companies realize multi-million-dollar value from monetizing their data and deploying the right analytics. You can contact the author at [email protected] or at

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