5 tell tale signs of bad data management

5 Tell-Tale Signs of Bad Data Management

Data management can be understood as the practical strategies you implement to collect, analyze, organize and maintain valuable information.

We use effective and standardized data management policies and procedures to reduce human error and build trust to meet consumers’ needs and market demands. 

In the business world, data management makes an impact by providing decision-makers with structured reports necessary to promote, target, or simply manage your product or service better.

To achieve business success using data, you have to apply solid data strategies that successfully achieve your organization’s goals.

A solid data management strategy reflects higher profit margins and heightened productivity without drastic changes in operations.  

So, how do you know when your system is failing? 

I have prepared five tell-tale signs that indicate that your data management operations are failing…

1.Data Collection – “Are there trust issues regarding the source of your data?”

Collecting data from various or limited sources can frequently leave you with scattered and unreliable reports driving inconclusive results.

If you’re asking the question, “Can I trust the information on this report?” then you indicate that your data is misleading. 

When off the mark data is gathered, it means that you might have:

  • Unclear Business Objectives 

If your organization’s KPIs are unclear, you tend to collect data that is questionable and asymmetrical to your business needs.

  • Inconclusive and Untrustworthy Results from Data 

Reports that are compiled from skewed data generate ill-informed decision-making.

  • Low-Quality Data 

Your data source has distributed unclear or unrefined data that your system cannot process accurately.

  • Dynamic Data

It is important to remember that data needs to be continuously updated to produce quality reports. Shaping your data to meet your goals over time is a quintessential step when churning out usable reports.

Internal Solutions

Using data collection methods that are aligned with all teams and KPIs assists in reducing cost, human errors and saves time. Needless to say that it helps to strategize better. 

Choose leaders in each team to identify the goals and objectives of each department so that the data you decide to collect is clearly defined and there is accountability. 

Use platforms like DialPad to collaborate with team members remotely. Take advantage of communication tools to optimize data collection and analysis. 

Create transparency by implementing systems where data is accessible and visual tools like dashboards and charts to increase data readability and information absorption.

External Solutions

Monitor customer behavior, study trends, and promote trust and loyalty to your brand by using business intelligence solutions to collect data.

When you are persistent in your effort to collect customer intelligence data regularly, you gain insight on how to manipulate your goals and adjust marketing strategies in real-time. 

Drive up your ROI by using customer relationship management technology to study your competitors’ offerings and determine why customers are attracted to them. 

A survey done by McKinsey shows us that value must be placed on customer analytics. Create an organizational culture that focuses on fact-based decision making, ensure that the analytics collected is of superior importance and use senior management resources at every stage of customer analytics to grow your business. 

Then use customer intelligence data to meet market changes and demands, and refine or tweak your product or service.

2. Data Preprocessing – “Do you feel like you are using wrong or incomplete data?”

Datasets must be cleaned and refined to identify the outliers, duplicates, and incorrect or missing values for obvious reasons.

When unexpected changes or errors occur in your data reports, you find:

  • Erroneous Data Reports 

When using automated data processing solutions, if values are not entered accurately, it spits out ineffective reports.

  • Time-Consuming Data Entry

Capturing and cleaning raw data is a tiresome and laborious process that can cost an arm and a leg if not done correctly the first time. 

  • Missing Data Leading to Deviant Results

Missing data can be owed to customers’ lack of enthusiasm to provide feedback or an employee’s forgetfulness to document the appropriate data or even system malfunction. 

Solutions

With the availability of raw mass data, the preprocessing phase of data management needs a streamlined approach. 

Automating data preprocessing prevents inaccurately produced reports as procedures will be systematic and centralized. 

You can also use real-time data mining strategies that analyze substantial datasets to effectively generate reports that convert to meaningful information.

Delivering almost a million pizzas a day across 70 countries is a great example of how Domino’s Pizza company used data mining strategies. They collected big data from 85,000 sources, both structured and unstructured, and leveraged the data to improve their product and service.

Recently they introduced a car delivery service that can deliver 80 pizzas per trip; the company is adamant about reducing their carbon footprint and keeping their customers happy. 

3. Data Inputting – “Are you using multiple platforms to suit all your needs?”

When inputting data into automated data analytics and business intelligence systems, you have to be mindful of the data warehouse you collect your statistics from. The system relies on cleaned data sources. 

The majority of the systems out there will not differentiate or evaluate reliable inputs or sources. It is not their job to perform. 

When using multiple platforms to integrate and process data, problems that might arise are

  • Having Inadequately Trained Employees

Data inputting relies on recruiting well-trained employees who have clearly defined roles and know what expectations they have to meet. 

Entrusting an employee with data inputting means that they have the skills to check and recheck data entry points and statistics. 

  • Experiencing High Costs

If the bill from your internal data management team makes your heart skip, then your in-house data management solutions are simply too costly. 

Several companies offer data management software; it is up to you to choose the one that best suits your pocket. Prices range anywhere from $99.95 to $10,000 and even higher, depending on the subscription you pick (based on your need). Do keep an eye out for data management software solutions that have free trial subscriptions.

  • Outdated Technology

The technology chosen for an organization must constantly be up-to-date to keep up with market demands. 

Continuously investing in updates for internal data management systems that keep up with advanced technology trends may exhaust the budget you set aside for your team.

  • Irresponsible or Inconsistent Data Management Roles 

Data inputting requires hyper-specialist employees. In an organization where clued-up employees are scarce, you will notice inconsistencies in the responsibility taken for data management. 

Solutions

For speedy and reliable results, an organization should outsource data management to the experts. But first, you must spend time choosing the right vendor. Study your business needs and have clearly defined expectations before you start searching for a data management and analytics solution (software or professionals).

Randy Bean, the author of Companies Are Failing in Their Efforts to Become Data-Driven’, said “Firms need to take a hard look at why these initiatives are failing to gain business traction, and what actions must be taken to reduce the cultural barriers to business adoption.

We can gather from this that scrutiny of the business intelligence system is of the utmost importance. We must investigate organization alignment, data inputting capabilities, visual tools, usability, and access speed to identify if the engine can manipulate data to suit your operational expectations. 

4. Data Processing –  “Help! I don’t understand the report.”

The worst possible outcome of the sizable investment you make in a data management system is receiving ambiguous reports. 

Data processing must generate valuable reports so you can transform them into actionable steps to make improvements and meet your business needs.

If your data does not convert as expected, you might experience:

  • Deviant Results on Reports

If the report does not give you statistics to make informed decisions about optimizing business operations, then you may have inputted unclean or inaccurate data. 

  • Unstandardized SOPs

Every entity that’s a part of your data processing activities must have standardized procedures and distinct responsibilities.

Datasets that do not comply with procedures will resist practical transformation for proper use in your organization. 

Solutions

High-quality data reporting begs for higher-grade input and analysis at every layer of processing. 

With the risk of human error, many organizations are now rushing to automate their systems to generate more pleasing and quicker results at lower costs.

Clean, merge and transform your data in real-time by relying on the capabilities of automation tools such as ClicData. These systems offer flexibility and modification when tracking targets, budgets and maintaining collaboration. 

5. Data Outputting and Storing – “Why is this report not giving me the information I need?”/ “Where did the data go?”

If you ask yourself any of the questions mentioned above, you have likely processed the wrong data. 

If this is the case, you will be able to identify the following problems:

  • Unusable Reports

If the report provided by your team does not support the changes, you intend to make, it is considered a botched report.

  • Poor Data Governance 

Enterprises that lack or disregard data governance standards lose their competitive advantage.

Poor governance arises when there is non-compliance to standardization. Insufficient quality data is fed in and ultimately affects productivity when meeting targets. 

  • Security Breaches

All data collected must be kept safe, as you are using personal information and analytics that can predict and affect competitors and customers. Remember the recent Facebook trials?

  • Corruption

Data is gold. Losing historical data due to improper storage like faulty hardware or an unsecured cloud could set your future analysis back by a mile. 

  • Inaccessibility

Using cheap or dodgy storage solutions to secure and manage data will cause inaccessibility problems when the information needed is time-sensitive.

Solutions

Always use best practices! Invest in a storage solution that you can trust to deposit and secure data for future outputs. 

One of the most dependable solutions is to use cloud storage to document historical datasets. Alex Clement recently shared results from a pulse survey which concluded that STaaS is on the rise as a solution to traditional data-storage systems to boost and optimize organizational performance.

Use properly stored data that is reliable and well-managed to make predictions that will catapult productivity, profitability, and trust in your organization. 

When information is transparently sheltered, it enables the organization and the consumers to make data-driven decisions that leverage the investment anyone makes.

Final Words

Every business activity needs data to increase efficiency, rectify errors, and wholesomely inflate profitability. 

Activities like marketing, sales, finance, accounting, etc. might have different data inputs, processing, and outputs. Still, at the end of the day, everything related to data must be coherent across all teams. 

Hence using an integrated data management and analytics tool like ClicData that has mastered the art in ‘everything data’ will be the one-stop-shop solution you are looking for.

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