The Importance of Data Quality

As the technological landscape changes there are few issues more critical than data quality.  Yet, despite the increased importance of data and the role that it plays in organizations, the question of quality remains a hurdle for many, one that they struggle to overcome.

Recently Forbes, working with KPMG, came out with a study that underscores this. About 24% of those surveyed claimed that they faced issues of data quality and accuracy, and that it presented an obstacle for them to overcome in order to reach their goals. Of the analysts and users that were included only 42% were confident in the quality of data. It is perhaps not terribly surprising then that 44 percent believed that their in-house analytics were not of the same caliber as third-party partners. This leads to increased costs for businesses, to the tune of $9.7 million annually according to Gartner.

The problem is that with the increased importance of data, those who do not put either do not put a high premium on quality or lag behind in the management of their data find that they are at a competitive disadvantage. Missed opportunities, and poor strategic decisions can mount, creating obstacles that are hard to overcome.

This means that the total benefit of actionable insights is never quite reached, and the integrity of the data is compromised in a way that undermines confidence in it.

In a sense it’s like playing darts with a blindfold. You may be pointed in the right direction, you may have the darts in hand, but the chances are any bullseye you may hit is going to be purely coincidental, rather than intentional.

So how is that avoidable?

Through strong data governance.

Data needs to be viewed as an asset, as well as a resource. It needs to be handled as such. Just as there are procedures in place that are utilized to manage an organizations other assets so too it needs to be with data. Processes need to implemented that allows for it to be properly and routinely checked and maintained to the standards that are necessary to ensure that it is not only of the highest possible quality, but that it is also useful. This includes a clear chain of command of people with the necessary skills to handle the data, implement best practices and turn it into actionable insights for best uses. It also means establishing clear responsibility for who is in charge of the care and quality the data that the organization collects.

What should be remembered is that a framework is being put into place that can be used to increase productivity as well as better inform decision making across departments. It’s cross-functional nature means that the effects of poor data quality can reach across different platforms, different divisions and have a profound effect on more than one might initially believe it would. That is what makes the situation as bad as it is. Whereas one might believe it can be contained in one area or one aspect of an organizations performance, it doesn’t. In a best case, Even if it is diluted as it moves, essentially isolated to one area, the truth is that it still will inform false narratives across different aspects of the organizations decision making process as nothing tends to live in a vacuum. In a worst-case scenario, it isn’t diluted and moves through, infecting the thinking of decision makers.

In the end it is about how you are going to be successful with your data, how it can help your decision making process and serve as a powerful tool at your disposal. That can’t happen with poor quality data.

The best advice that an organization can get is to ensure data quality is maintained to the highest of standards. Realize we have transcended the age of spreadsheets, and invest in the infrastructure necessary to get the job done right. Yes, it is an investment, but if you don’t make that investment early you are going to regret it later when your competition has the people, procedures and software in place to effectively utilize this asset and your organization lags behind.

Remember data isn’t just about who has it. Any organization can collect data about anything now. It is about who has the best data with clear vision of what it means and what it should do and who knows how to integrate and implement it to create value. If your organization isn’t doing that it is time to re-evaluate.

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Building a More Data-Driven Culture

At some point, every company asks themselves the question, “Am I succeeding as well as my competition?” In a competitive landscape, no one operates in a vacuum.  If you are not operating at the same level as your competition, then you are looking for ways to improve in order to gain a competitive edge or advantage. It quickly becomes a game of process improvement, which can occur in many forms such as brand awareness or supply chain and logistical enhancement. Yet, at the crossroads of all process improvement questions lies data. Data is fast-becoming the new disruptive force in our changing world. The company that, not only has access to the right data, but knows how to utilize it correctly, can be the one that rises above the competition.

Perhaps this seems obvious to some; however, many executives still fail to realize the power of data analytics. Companies that try to transition to a data-driven culture face enormous internal resistance. In fact, according to the New Advantage 2017 Big Data Executive Survey, 85% of the executive-level respondents confirmed that they were trying to adapt their organization into a more data-driven culture. However, only 37% of those respondents believed that effort actually flourished. In in an ever-increasing, data-driven society, companies need their data-focused strategies to flourish, if they are to develop or maintain their competitive advantage.

So, how does an organization grow and nourish a data-driven culture? These simple, five-steps can help organizations achieve their goal.

1. Avoid Faulty Assertions and Personal Bias When Driving Decision Making

Data analytics is not about “What your gut is telling you” or “What you believe.” It is about  undisputed, provable fact inherit within the data. Often, executives lead with similar statements that are based on assumptions and theories, rather than fact. Can an executive have a gut feeling that is confirmed by data? Absolutely, but leading by gut feeling translate to leading by assumptions based on incomplete information or pre-conceived notions. If you want to make better decisions, then you need high-quality data and stronger data analysis. This means setting aside personal bias and utilizing the resources that you have to guide your decision making through an evidence-based thought process

2. Treat Data as a Resource, Rather than Just a Tool

When data is used frequently, it can easily be treated as a tool. The problem with the tool-based mentality it that it does not associate value with the data. By establishing the value of data early in the analysis process, a framework of priority is established for cleaning the data, updating datasets through collection, assuring quality control, closing potential gaps, and defining key performance indicators. Emphasizing the value of data can lead to sounder, more solid, decision making.

3. Don’t Overcomplicate It

Overcomplicating the data analysis process can lead an organization to feeling intimidated and overwhelmed. People will resist data analysis implementation if they feel overwhelmed by the tasks and deliverables. Organizing data in a simple, well-structured location is an important step that can minimize frustration and angst during the analysis process. Centralizing the data allows teams to integrate data sets, provides greater accessibility and increases usability. A complicated system can discourage team participation and create frustration, but a clean and simple system can support and even encourage team participation by providing value to the team.

4. Incorporate Meaning Beyond the Numbers

It is not enough for organizations to know their numbers. They need to know what those numbers mean for the future of the organization. Data analysis must be actionable. Set tangible goals or establish a clear mission that aligns with data reporting efforts. The key performance indicators (KPI) must be applicable and relevant to the organization’s strategy. Simply phrased, data informs decision-making. If the data collected and reported does not enhance decision-making, then it is a wasted effort that can be costly and ineffective.

5. Turn that Data into Action

Being informed is not enough, and having actionable-data is not enough. The organization must act upon the information! In order to foster a data-driven culture, the organization must leverage the information by taking action. Only then, will the data collection, analysis, and reporting efforts have value. Too often organizations are held back by inaction. They never leave the well-informed, theoretical phase of chasing the next big thing. Data might inform decision-making, but it can not force a decision or action. That is the responsibility of the leadership team.

The world is constantly changing, and the business environment is always evolving. Organizations must adapt to the change if they want to build or maintain a competitive advantage. This means reaching beyond an understanding of the importance of data and learning to leverage data for strong decision-making. Only then, can an organization chart an evidence-based strategy that yields results.

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