Did you know that individuals and companies worldwide create at least 2.5 quintillion bytes of data every day? Technology has boosted productivity and effectiveness in virtually all facets of modern business. Data creation, for instance, has experienced a meteoric rise, with some estimates suggesting 90% of the world’s was generated in the past decade.
Given such sheer abundance, many companies struggle to make sense of the heaps of data they generate/receive every day. In fact, recent studies reveal that most brands use less than half of their structured data and less than 1% of their unstructured data during decision-making.
What Is Data Fatigue?
Data fatigue is a phenomenon characterized by brands accumulating data faster than they can analyze it and generate actionable insights. Usually, the gathered data is too flawed or overwhelming for the teams responsible to make sense of it promptly.
How Can Brands Combat Data Fatigue?
1. Leverage Data Visualization Tools
Company executives have to dive deep into the finer details of the data they receive from time to time. However, most managers and trustees need easy-to-understand, high-level, real-time information to assess progress and derive actionable insights on a day-to-day basis. Enter cloud-based data visualization.
Using cloud-based data visualization tools, managers can access clear, relevant, up-to-date information as events happen so they can make the right decisions without feeling bogged down. In addition, data visualization tools come with filters that exclude unnecessary data, consequently preventing choice overload.
2. Data Governance
Data governance is a set of policies and guidelines that companies implement to manage their data. The ultimate aim of data governance is to ensure company data is available, high-quality, consistent and usable by implementing frameworks that minimize waste while maximizing data effectiveness. An effective data governance initiative should clearly outline:
- The parties that own the data
- Everyone who can access and modify the data
- The internal systems that maintain the ‘master’ customer file
- The audit process that ensures good data is not modified without review or overwritten by faulty data
3. Data Hygiene
Data hygiene is a series of processes followed to inspect and cleanse data. According to industry statistics, at least 54% of sales professionals do not clean their data before entering it into a database. This statistic alone shows the height of bad data hygiene practices.
While some organizations address their data hygiene issues by employing data scientists, many small companies simply lack the resources to maintain an in-house data science team. Fortunately, it is possible to outsource this service to a dedicated solution provider. Data hygiene providers clean, de-dupe and enrich data, effectively saving their clients from wasting valuable time and effort sifting through heaps of non-essential data.
4. Centralized Data Stewardship
Centralized data stewardship – having a single data owner supported by several data professionals – has proven to be an optimal approach to data management. Decentralized data stewardship creates room for executives and departments to develop incompatible data strategies for different processes. Not to mention, decentralization can lead to siloed data, i.e., individual departments lacking access to all the relevant data, limiting their ability to draw comprehensive insights.
Due to the vast amount of information generated daily, data fatigue has become a serious yet highly unacknowledged issue. Thankfully, data can be an incredibly powerful tool for companies that choose to stay smart and selective about the information they need and their data management practices.