Analytics Graveyards: Reviving Unused Data for Business Growth
Halloween spooks approaches, and brings with it the whispers of ghosts and everything supernatural. However, in business, the scariest creatures that lurk behind the scenes aren’t spirits or monsters. They are datasets lying dormant in what we refer to as analytics graveyards. This data is neglected, unutilized, and awaits revival.
The question though is:
Can your organization breathe new life into this dead data and use it to spur growth?
Kind of like the Frankenstein’s monster but useful!
It is the spookiest corner of business intelligence, and we will look at ways to resurrect forgotten data to harness its potential and drive growth in your business. This world of data offers plenty of opportunities to find hidden treasures and eliminate the shadows of inefficiency.
The Haunting Reality of Data Graveyards
In a data-driven economy, companies can collect vast amounts of data – from consumers, vendors, partners, operations, and even marketing. However, the majority of it remains unutilized in storage, similar to a forgotten relic in a haunted attic.
Research shows that more than 73% of the data collected by companies is never used for analysis. Whether it is old customer records, lost transactions, or metrics that have been forgotten about, dark data represents lost opportunities to grow.
The problem is two-fold. Companies either don’t recognize that they have valuable data or lack the resources to mine it effectively to gain insights. The reason for this is usually due to data being dispersed between departments or confined to silos.
According to a research, more than 70-90% of data from enterprises could be dark data – data that has been gathered but not analyzed.
If you have the right strategies and tools, revitalizing this data can give your business a competitive advantage.
Raising the Dead: Why Unused Data Is a Goldmine
Revitalizing data is not just about scrubbing old numbers clean; it is about bringing out new perspectives and creating growth opportunities. Similar to finding gold nuggets in a dark mine, if businesses tap into data that is not being used, it can be used to:
Discover hidden patterns and trends
When coupled with data from the latest sets, the old data may reveal patterns that were not apparent earlier. These trends are crucial to understanding customer behavior, identifying shifts in market dynamics, and enhancing business processes.
Improve decision-making
By reviving old data, businesses can gain greater insight into their operations. A wealth of data from the past can help leaders make better decisions based on long-term trends rather than snapshots.
Enhance customer experiences
Information that tracks all phases of a customer’s life—from acquisition to retention—gives businesses an all-encompassing view of their customers. Businesses can use this information to customize interactions, target particular segments, and develop more effective marketing strategies.
Increase efficiency
By studying operational data, businesses can detect inefficiencies and streamline processes. For instance, by looking at long-unused data from the supply chain, companies can identify ways to cut costs and improve delivery time.
Reviving Dead Data: Breathing Life Back into Neglected Business Assets
The forgotten data sets can be like a haunted house filled with treasures and lost potential. Here is how you can restore this “dead” data and bring it back for meaningful use:
Step 1: Unearth the Data – Conducting a Data Audit
This involves sorting and categorizing the information your business has accumulated over time. Like looking through the shade and the gloom for clues, data audits can reveal assets that have often been buried.
Here’s how:
Identify Data Sources: Begin by determining where the information is stored, whether in customer relation management (CRM) systems sales logs, sales reports, or spreadsheets from the past. Do not overlook structured and unstructured information from social media, emails, and call transcriptions.
Assess Data Quality: Not all data is produced equally. Once you have identified the sources, examine how reliable the information is. Does it have the correct information? Is it comprehensive and relevant to your business goals? Incomplete, messy data is like a zombie and does no good.
Map the Data: Utilize data-mapping tools to discover the relationship between different data sets. What do your sales numbers of five years ago align with your current retention rates? Do you have the ability to merge the data from your old campaigns with the latest insights into customer behavior?
For instance, after conducting a comprehensive data audit, Netflix discovered that user data from its DVD rental days could be used to fuel its recommendation algorithm. By unearthing and categorizing years of customer behavior data, Netflix was able to predict user preferences more accurately, ultimately contributing to the success of its streaming platform.
Step 2: Clean the Data – Removing Ghosts of Inaccuracies
Once you have found the information, the next step is to clean it.
This includes removing duplicate records, fixing mistakes, and filling in the gaps in the data. Imagine this process as the removal of ghosts from a mansion. Ghostbusters 101 -once ghosts are eliminated, you can concentrate on the valuable information left.
Data cleansing tools can automate many of these steps, aiding you in standardizing data formats, removing duplicate records, and filling gaps when needed. Clean data is crucial to ensuring the accuracy of your analytics information.
Step 3: Analyze the Data – Summoning New Insights
This is the point where magic occurs. Utilizing the latest analytics techniques, businesses can discover patterns and gain helpful insight from data.
Here are some of the methods of analytics that could assist:
Predictive Analytics: Using machine learning models to predict future outcomes based on previous data. For example, you could predict customer churn and the demand for certain products in the near future.
Customer Segmentation: Examine older customer data to find distinct customer groups. This will help you target your marketing strategies, enabling you to reach the most relevant people with the appropriate message.
Operational Analytics: By analyzing historical operational data, companies can pinpoint bottlenecks in their processes and optimize them for efficiency.
Amazon is an example of using predictive analytics to enhance operations. By analyzing past purchase behavior and customer reviews, Amazon’s machine learning algorithms predict what products individual customers are likely to purchase next. This helped them create personalized recommendations that account for a significant portion of their sales.
Step 4: Visualize the Data – Bringing It to Life
After you have rediscovered your data and discovered important insights, it is time to display them in a manner that is engaging for all involved. This is where data visualization comes into the picture. An effective graphic or dashboard can turn boring data into something interesting and useful.
Data visualization tools enable users to design interactive graphs, charts, or dashboards, which make it simple for decision-makers to comprehend the complexity of data. These tools will empower your employees to make quick and informed decisions.
Airbnb effectively visualizes data to drive decision-making. Using interactive dashboards, Airbnb visualizes booking trends, customer preferences, and property performance data for hosts and internal teams. These visualizations enable the company to understand market trends, optimize pricing strategies, and enhance user experiences across its platform.
Step 5: Integrate and Act on Insights – A Continuous Cycle
Reviving data is a continuous process. As your business grows, so does the data graveyard. It is essential to integrate the data you have rediscovered with your existing analysis and make data-driven decisions the core of your business plan.
Establish an environment of data literacy in your company in which employees from different departments can access, analyze, and take action on data-driven information. Encourage department collaboration to guarantee that every employee operates with the same approach.
All modern successful companies have laid a strong foundation by relying on data visualization, predictive analytics, and AI/ML solutions. Deploying these solutions is more than a choice; it is a necessity to carve a competitive edge in a crowded marketplace.
Summing up: Reviving Data for a Spooktacular Business Future
As you celebrate Halloween, make it a point to reflect on your company’s analytics graveyard. The spooky season should be a considerable motivation.
The information you have gathered throughout the years is not as dead as you think.
Your company can breathe new life into its dataset by conducting data audits, eliminating mistakes using advanced analytics, and creating captivating visualizations. The latest insights you gain will lead you to make better decisions that optimize your operations and position yourself better on the market.
When celebrating Halloween, be aware that the true treasures are not hidden in the trick-or-treat bags but in your company’s long-forgotten data graveyards. With proper tools and strategies, you can transform these haunting objects into valuable assets that can drive growth for your business.
Ready to revive your unused data and turn it into actionable insights? Lumenore can help you breathe life into your analytics graveyard and unlock growth opportunities. Don’t let valuable data go to waste — contact us today to discover how we can empower your business with data-driven decision-making!
