“You get recessions, you have stock market declines. If you don’t understand that’s going to happen, then you’re not ready. You won’t do well in the markets. If you go to Minnesota in January, you should know it’s going to be cold. You don’t panic when the thermometer falls below zero.”

Peter Lynch

Prediction about global recession

Economists are warning about both: a U.S. and a global recession. Declining stock prices – especially in tech and retail, rising interest rates – and increasing issues with supply chain are main indicators inflating this prediction.

With an impending sense of doom in the market, businesses are making rash, impulsive decisions. New projects are halted, expenditures are reduced too low, and employees are laid off brutally. The unpredictability of future events is adding to the worry of business leaders as they try to navigate through a possible recession.

In the midst of it all, data proves to be the single most valuable asset of an organization that offers real value in an economic downturn.

Data: The truth is out there

Data becomes a savior in times when all else fails. A quick look at what went down during the previous economic downturns can help you navigate the present with confidence. Data offers a cushion to businesses and empowers them to make decisions with a sense of familiarity and comfort that is needed in unprecedented times. But it is obvious that raw data doesn’t offer the required insights, and it must be transformed into business intelligence and actionable items.

Business intelligence cycle

Research on Data and Disaster describes a simple yet powerful business intelligence cycle:

The cycle shows how raw data is changed into actionable insights:

1. After raw data is collected, it is converted into information by verifying its metadata and testing values for quality to rule out inaccuracies and inconsistencies.
2. Information is then converted into knowledge by feeding it to business intelligence tools.
3. Knowledge is then managed to form business plans and goals.
4. Leaders get together to convert those plans and goals into actionable items.
5. New raw data is collected again after actions are taken, and then converted into information.

6 ways data helps in recession

Accurate and reliable business intelligence helps businesses to make decisions based on real-life data, rather than on guesses and assumptions. Here are 6 ways in which data helps companies to stay afloat during a recession.

1. Minimize risk

Two decisions can lead you down separate pathways; but how do you know which one will bring about a better, more positive impact for your business? The answer is in the past data. Analyzing past information can help you avoid costly decisions and measure up opportunity costs of various pathways – empowering you to choose alternatives that offer more value in the short term.

2. Plan resources

One of the earliest decisions business leaders make in an economic crisis is to lay off a big number of their employees. But historical data has shown that such decisions are always made too early. For example, with the onset of COVID-19 pandemic, the world experienced the shortest ever recession that only lasted 3 months. And business leaders soon realized that employee cuts were made too early as they found rehiring, onboarding, and training employees to be a far greater challenge than retaining them.

3. Predict recession severity

Recessions always feel gloomy, long, and severe. But past data shows that it is not as bad as you may feel while going through one. Because the timelines of when the recession will hit, how long will it stay, and how severely it will impact small and large companies was not according to how economic gurus were predicting it. Using data to really understand these aspects of the recession can help base decisions on more accurate information.

4. Read past success stories

No matter how bad recessions sound, there have been success stories of businesses not only surviving one, but also thriving during and after it! The secret is in the decisions they made before the recession hit. You can start by getting your hands on such past success stories or even connecting with leaders that have made it through previous economic crises and learn how they did it.

5. Observe consumer behavior

Businesses in the supply chain or retail space complain about the biggest recession downfalls. But the truth is, there have been success stories of how small retailers grew big during difficult times. The main secret here is to understand consumer behavior. It is not that consumers do not buy during an economic crisis – it is that they may buy something different and in varying amounts depending on the economic state of their country.

This is the best time to invest in market intelligence platforms that give you the latest market insights. Read more about how retailers can continue to ride the ecommerce wave during economic downturns.

6. Invest in operational improvement

Data can help you to understand which business areas require operational improvement. Since business is slow, it’s a good time to analyze past operational transactions and design new, enhanced business processes for different areas, such as customer experience and engagement, sales cycle, supply chain management, etc.

Poor data quality: The truth is not out there

It is imperative that data used to build a recession survival plan needs to be accurate, valid, and consistent. But in reality, data is full of errors and intolerable defects that make business intelligence quite difficult, if not impossible. Poor data quality – if not managed in time – has proven to produce unreliable results and have a devastating impact on a business.

Business intelligence cycle without data quality

Let’s take a look at how a business intelligence cycle performs when bad data is fed to it:

1. The most important step of converting data into information is skipped.
2. Analysts and BI tools try to directly extract knowledge from dirty data.
3. The “knowledge” is then converted into business goals and plans.
4. Leaders design actionable items from the polluted business plan.

Thus, leaders order their teams to take action based on a plan that has nothing to do with reality. Not only that, all the time and resources expended for this BI cycle were wasted since the input was corrupted to begin with.

4 ways poor data quality ruins a recession survival plan

Let’s take a look at how poor data quality can ruin a company’s recession survival plan.

1. Unreliable insights from BI tools

We just saw how dirty data can destroy your business intelligence insights. With bad data fed to your BI tools, leaders may experience inconsistent and confusing suggestions from their BI tools or team of analysts. Basing decisions on such insights can lead your organization to miss critical market opportunities and lose revenue in difficult times. This can be devastating for your business as it may not be ready to withstand such losses.

2. Disengagement with customers

Businesses competing in a market for decades have a good understanding of their consumers – in terms of demographics, their preferences and choices. But an impending recession may change that. Observing consumer behavior from outdated or misinterpreted data can be damaging for your reputation in the market. Your customers may feel like you are losing touch with them and not delivering according to their expectations. This can lead your competitors to steal your customers as you try to cut down on customer services and support.

3. Roadblocks in shift to digital

Businesses often halt their digital transformation initiatives in fear of a potential recession. But economists have predicted that recessions are a good time to accelerate digital transformation projects since their opportunity costs are low. This happens because business is already slow and hiring technical professionals is easier and less costly as they are being laid off across the tech industry.

Despite its benefits, companies are stuck in their shift to digital due to mountains of missing, incomplete, inconsistent, and unstandardized information. When data quality is not up to the required standard, it causes long delays as businesses try to digitize processes or introduce new technology.

4. Reduced operational efficiency and productivity

Since business is slow during such times, companies tend to focus on improving operational efficiency across the organization to focus on new expansion opportunities in the market. But poor data quality causses serious bottlenecks in everyone’s work as they have to double check data sources and content before using it in routine operations. Low operational efficiency and productivity levels result from such problems at a time when your business can least tolerate them.

A data quality plan before recession hits

There is no doubt that your analytics need to be timely and accurate to survive a recession. But poor data quality can destroy both the timeliness and accuracy of your insights. For this reason, it is imperative to invest in data quality management right now so that you can dodge the potential downfalls of bad data when the recession actually hits. Let’s take a look at the 3 most important steps in dealing with poor data quality when we’re nearing a recession.

1. Identify data quality issues

The first step is obvious: figure out what you’re dealing with. Not every business has the same set of data quality issues. Data quality is defined as fitness of data for any intended purpose. Depending on how data is used at your company, you may find many discrepancies in how data quality is being managed. A list of common data quality issues is given below. To know more, check out 12 most common data quality issues and where do they come from.

No.	Data quality issue	Explanation	Example of data quality issue
1	Column duplication	Multiple columns are present that have the same logical meaning.	Product category is stored in two columns that logically mean the same: Category and Classification.
2	Record duplication	Multiple records are present for the same individual or entity.	Every time a customer interacts with your brand, a new row is created in the database rather than updating the existing one.
3	Invalid data	Data values are present in an incorrect format, pattern, data type or size.	Customer Phone Numbers are present in varying formats – some are stored as flat 10 digits, while others have hyphens, some are saved as a string, while others as numbers, and so on.
4	Inaccurate data	Data values do not conform to reality.	Customer Name is incorrectly stored: Elizabeth is stored as Aliza, or Matt is stored as Mathew.
5	Incorrect formulae	Data values are calculated using incorrect formulae.	Customer Age is calculated from their Date of Birth but the formula used is incorrect.
6	Inconsistency	Data values that represent the same information vary across different datasets and sources.	Customer record stored in the CRM represents a different Email Address than the one present in accounts application.
7	Missing data	Data is missing or is filled with blank values.	The Job Title of most customers is missing from the dataset.
8	Outdated data	Data is not current and represents outdated information.	Customer Mailing Addresses are years old leading to returned packages.
9	Unverified domain data	Data does not belong to a range of acceptable values.	Customer Mailing Addresses are years old leading to returned packages.

2. Implement a data quality plan in weeks

If your datasets are polluted with errors, you need to utilize a data quality platform – but nothing too grand, something that can be up and running in a matter of weeks and not months. There are multiple ways vendors package various data quality management processes in their tools, such as:

• Data profiling to assess the current state of data quality,
• Data cleansing and standardization to eliminate null values and noise, and transform data into a standard view,
• Data matching to identify records belonging to the same entity,
• Data deduplication to eliminate duplicate records,
• Data merge and purge to retain useful information and merge records to attain the golden dataset – free from errors.

3. Shorten the action-to-impact cycle

When it comes to implementing a data quality tool, many companies get stuck in advanced data management systems that take care of complex data management principles, such as data governance, centralized management, master data management, as well as data protection and security. Although these are great features to have integrated into your data systems, they may take a long time to implement and prove beneficial for your business.

Focus on minimizing your action-to-impact cycle. During economic downturns, you probably want something that gives you a quick yet detailed overview of data quality errors existing in your datasets and the easiest way to resolve them.

Final thoughts

Economic unpredictability makes business leaders fearful of future events. Business and market intelligence can offer them the comfort needed to make crucial decisions. Investing in BI tools and a team of analysts is detrimental in these unprecedented times, but we cannot undermine the value of clean data – the asset that is transformed into actionable insights.

For starters, providing self-service data cleansing and matching tools to your teams can be very beneficial to produce fast results. An all-in-one, self-service tool that profiles data, performs various data cleansing activities, matches duplicates, and outputs a single source of truth can become a big differentiator in the performance of BI tools and data analysts.

DataMatch Enterprise is one such tool that facilitates data teams in rectifying data quality errors with speed and accuracy, and allows them to focus on more important tasks. Data quality teams can profile, clean, match, merge, and purge millions of records in a matter of minutes, and save a lot of time and effort that is usually wasted on such tasks.

To know more about how DataMatch Enterprise can help, you can download a free trial today or book a demo with an expert.