Google tells the story “big data,” and autocomplete for search: “Big data is the future. Big data is the new oil. “
These are some exciting statements, but what is often lost in conversations about big data is the high cost bad Data.
If your company prides itself on making data-driven decisions, it is important to recognize that those decisions will only be as good as your data. The cost to the US economy is $ 3.1 trillion per year due to poor data quality, and it is causing a crisis of confidence in many industries.
According to a recent Gartner report, more than half of senior marketing leaders are disappointed in the results that have come from investing in data analytics. As a result, only 54% of their activities are affected by the data. By 2023, Gartner predicted that CMOs were downsizing their analytics teams without any expectations.
The importance of quality data cannot be understated, but often leaders do not know where their data collection and analysis is breaking down. Here are three data-quality issues you may not be aware of …
Anomalies become difficult to manage as data balloons
Often, the data does not follow a logical pattern. This does not mean that your data is not accurate, but outliers (such as seasonal fluctuations) should be accounted for.
If you own an apparel company that sees a huge demand for red sweaters in lead for Christmas, you can easily identify the root cause and handle it appropriately. However, completely removing your outliers is not the answer, as some departments may require external information such as your buying and selling teams.
This gets a lot more complicated as your company starts collecting and using more data. Each new metric will have its own trends and anomalies, and you cannot manually examine and adjust all these peripherals. As Credera’s chief data officer Vincent Yates stated in a blog post, “Classifying discrepancies in data is an art, and virtually no organization has any mechanism to codify these annotations globally. ”
Over time, the problem with the wrong data set produces a snowball effect. This data cannot be used to meet future demand with any accuracy, which destroys organizational confidence in the data.
Data models break down with volume
Just as unmanaged outliers can skew data over time, many data models break down when the amount of data increases. This does not mean that data models suddenly stop working. Most data quality issues exist within an organization from the outset, but they are not clear until they reach a certain scale.
This is particularly relevant now, as retail data has dried overnight due to orders to stay indoors. When restrictions are lifted, it is unlikely that consumer behavior will be the same as it was before. Customers can spend less or order more goods online. Many companies will find that their old data is no longer relevant.
Professor Angel Evan of Stanford University wrote, “Even businesses that had accumulated massive amounts of customer data prior to Kovid-19 are finding themselves in the same cold state as businesses in unknown markets Has been doing.”
In the coming years almost all companies will re-augment their data. They have to update their models to change consumer behavior.
Different departments use the same data for different purposes
Today, companies are producing more and more data, and that data is being used to inform company decisions at every level. Key performance indicators (KPIs) generated by the e-commerce team can be used by the marketing department to target a new customer segment. Or, the same data can be used to build models around financial performance at the highest level to inform decisions about hiring.
The trouble is that the people who create that data generally do not know who is using it or how it is being used. It is unclear who is responsible for the accuracy and management of that data. This becomes problematic when it is used to make decisions.
How to get a handle on data quality
Just as you create new challenges on a company scale, you should reevaluate your approach as you measure your data. Here are three best practices for improving data quality:
1. Appoint a Chief Data Officer. The Chief Data Officer (CDO) will be responsible for managing the company’s data and making plans to maintain data quality. He should be an expert to get insights from the data and give it context so that the rest of your team members can use the information.
2. Create a data strategy. Data is no longer just a supporting by-product of marketing and sales activities. Data is a property. Like any other asset, it must be constantly protected and managed.
Unfortunately, most companies keep their data and data activities silent in various departments. While they may cause excessive discussion about data quality or data security, there is no overarching strategy for how that data should be managed or used. This can lead to huge amounts of dark data and data decay.
A solid data strategy establishes how your company will manage and share the organization’s data so that it can make the greatest profit. This strategy should be scalable and repeatable.
3. As you update the data model. As your customer base grows and you start collecting more data, you or your CDO will have to continuously evaluate the model you use to make sense of that data. It is important to ask what you initially changed to that model and whether some metrics are still relevant. With this, you will be able to get the clearest picture along with clearing the number.
We cannot go once before big data, nor should we. Big data has helped us make huge progress in everything from self-driving cars to patient outcomes. In the coming year, it will tell us how well Kovid-19 vaccines work and which groups they work best on. But to make big data sustainable, you must first be proactive in improving the quality of data within your organization.