5 Reasons Why Data Isn’t Working in Your Organisation

5 Reasons Why Data Isn’t Working in Your Organisation

5 Reasons Why Data Isn’t Working in Your Organisation


 

The power of data cannot be overstated. We love data and you should too!  

Organisations in every industry, everywhere are relying on data to make informed decisions, drive innovation, and gain a competitive edge. And if you’re not… WTF are you doing? 

However, despite the potential that data holds, many organizations struggle to harness it effectively. 

In this blog post, we’ll explore what data is and how it can be used. After this, highlighting the 5 common reasons why data initiatives may not be working in your organisation and how to overcome these challenges. 

Data: What is it? How is it used? 

Okay, so what is data? If you didn’t know already (if you already know, skip this bit) 

Data refers to raw facts, figures, and statistics that are collected, recorded, or stored in various forms.  

It can take the form of numbers, text, images, audio, or any other structured or unstructured information.  

Data is the fundamental building block of information and knowledge. It can be categorised into two primary types: 

  • Structured Data: This type of data is organised into a predefined format and is easy to analyse. It’s often found in databases and spreadsheets, and each data point has a specific meaning. Examples include numerical values in an Excel spreadsheet or customer details in a relational database. 
  • Unstructured Data: Unstructured data is not organized in a predefined manner. It can be in the form of text, images, audio, or video and doesn’t fit neatly into rows and columns. Examples include social media posts, emails, images, and videos. 

Data is a valuable resource that can be harnessed for various purposes in different sectors. Here are some common ways data can be used: 

  • Decision Making: Data is crucial for informed decision-making. Organizations use data to analyse trends, identify opportunities, and make strategic choices. For example, a retail company may use sales data to decide which products to stock. 
  • Performance Analysis: Data can be used to assess the performance of processes, products, or individuals. In sports, for instance, performance data is used to evaluate athlete performance and make improvements. 
  • Predictive Analytics: By analysing historical data, organizations can make predictions about future events. For example, financial institutions use historical transaction data to detect fraudulent activities. 
  • Personalization: Data is used to tailor experiences for individuals. Online retailers, for instance, use data to suggest products based on a customer’s browsing and purchase history. 
  • Marketing: Marketers use data to target specific demographics, track campaign performance, and optimize their strategies. 
  • Customer Insights: Customer data helps businesses understand their customer’s preferences, behaviours, and needs, enabling them to provide better products and services. 
  • Financial Analysis: Financial institutions rely on data for risk assessment, investment decisions, and fraud detection. 

Data can be a powerful tool when collected, processed, and analysed effectively.  

However, you might be using data in your organisation. Yet, it is failing or not being used to its fullest potential!  

Here are 5 common reasons why may not be working in your organisation and how to overcome these challenges. 

Reason 1: Lack of Data Strategy

A clear data strategy is the foundation upon which successful data initiatives are built. Without it, organizations are essentially navigating uncharted waters.  

A data strategy encompasses a structured plan for collecting, storing, analysing, and using data to achieve specific business goals. It defines the what, why, and how of data management. 

It is basically a business strategy using data.  

The consequences of not having a clear data strategy are numerous. It often leads to confusion, redundancy, and a lack of direction. Without a strategy, you may find yourself collecting and storing data that is irrelevant to your business objectives. 

To overcome this challenge, look to organisations like Amazon and Google, which have well-defined data strategies. They use data to optimise their operations, enhance customer experiences, and drive growth.  

Create a data strategy that aligns with your business goals and ensure it is communicated and followed throughout your organisation. 

Understand more about data strategy here.  

Reason 2: Data Silos

Data silos occur when different departments or teams in an organisation store data independently, without sharing or integrating it.

This can hinder effective data utilisation, decision-making, and collaboration.  

Imagine marketing and sales teams using different data sources, leading to conflicting information and missed opportunities. 

To break down data silos, implement systems that allow for easy data sharing and integration. Encourage cross-functional collaboration and ensure that data is accessible to all who need it.  

Tools like data warehouses and collaboration platforms can be instrumental in this process. 

Understand more about Data Silos and how to eliminate them here.  

Reason 3: Inadequate Data Quality

Poor data quality can be a major roadblock to effective data utilisation. Have you heard the term s#!t in = s#!t out?? 

Inaccurate, incomplete, or outdated data can lead to misguided decisions and analysis. To mitigate these risks, organisations must prioritise data quality. 

Start by implementing data validation and cleansing processes. Regularly audit your data for accuracy and completeness. Establish data quality standards and make sure they are consistently upheld across the organization. 

Reason 4: Resistance to Change

Implementing data-driven practices often encounters resistance from employees or teams comfortable with existing methods. 

It’s essential to recognize that data-driven decision-making may necessitate changes in established workflows and practices. 

To address resistance, consider offering training and education to your employees. Show them the benefits of data-driven decision-making through success stories from other organisations. 

Create a supportive culture where employees are encouraged to embrace data and innovation. 

Also, with data automation your team’s output will increase and reduce costs – it is obviously the way forward. Make your team efficient and happy!  

Reason 5: Insufficient Data Governance

Data governance is the framework that ensures data is managed, utilised, and protected effectively within an organisation.  

Without robust data governance, data-related issues can easily spiral out of control. 

To establish effective data governance, define roles and responsibilities for data management, set data access controls, and enforce data policies and standards.  

This will ensure that data is protected and used ethically and responsibly. 

Conclusion

In conclusion, data is an invaluable asset for any organisation, but to unlock its full potential, it’s crucial to address common bottlenecks that may hinder its effectiveness.  

By implementing a clear data strategy, breaking down data silos, ensuring data quality, addressing resistance to change, and establishing strong data governance, you can transform your organization into a data-driven powerhouse. 

Obviously, these aren’t the ONLY reasons why working with data isn’t working within your organisation. But these are the most common reasons why!  

Stick around though, we may go more in-depth into your specific reasons soon as to why data isn’t working in your organisation!

Take a moment to assess your organisation’s data practices.  

Are any of the challenges mentioned in this blog post affecting your data initiatives? If so, consider taking steps to address them and unlock the full potential of data in your organisation.  

Fill out the form below to reach out if you have questions or need further guidance on any of these topics!

Let’s make data WORK! You need it to have a thriving business in 2024! 

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Data Masking

Data Masking

Engaging Data Explains:

Data Masking


The Data Masking Challenge 

One of our clients had an interesting data masking requirement. How to mask Production data to meet with GDPR and IT security policies. The data needed to be human readable enabling the development and testing teams to create a data feed for a new Client Portal system. However, the core system did not have the ability to mask the data, only scramble or obfuscate. The core system was extremely complex, built & expanded on over 10 years. It is difficult to understand the system & how data is stored because documentation didn’t exist!

Furthermore, the architecture restraints meant there was not enough storage space to hold a second (in-line) database with masked production data.

Is This A Common Problem?

The more companies we speak to, the more complex or complicated situations we find. From our experience, we’ve found a pattern emerging in the common problems or requirements:

  • Old Tech – Ageing trading platforms/core systems or sources of data often don’t have the functionality to masked data. Those that do or have extensions/plug in to mask the data often take a long time to process or do not have the flexibility to fit every scenario.
  • Quick turnaround – Near realtime data is nice to have, but not always a real requirement.
  • Specific/varied masking – Different types of masking needed, obfuscation, scrambled, encrypted or human readable & randomised.
  • Storage – Limitations on storage or infrastructure makes it difficult to store an entire copy of production. 
  • Cost – Large database providers offer alternative tools with the same effect but also command a very large price tag.
  • Time – Developers can develop hand-cranked specific solutions which take reasonable amounts of time to develop but much longer to test to ensure the solution is working as expected.
  • Doing the right thing – Most clients want to do the right thing to meet regulatory requirements but see this as a complicated housekeeping chore and recognize the risk but choose to ignore it.

Engaging Data Discovery

We had a lot of options to solve this problem, but selected Redgate Data Masker and here is why:

  • After a review of the underlying data structure, it was too difficult, costly & time intensive to try to transfer the data into the Test environment and apply masking rules.  
  • We discovered that it would take 32 to 48 hours to copy the “majority” of the data from Production to UAT environments. Doing this would copy most but not all of the data creating a potential for leaving things behind. Plus it would take more time to run the system’s own obfuscation processes (another 8 hours).
  • Masking not Obscuring. Create human-readable values. i.e. Mr. Smith converts to Mr. Jones. This was not available from the trading platform’s masking function.
  • Defined values. Create predictable values, such as a telephone number set format or date of birth.
  • There was a lack of documentation regarding the location of personally identifiable data. This could result in the process missing part of the system if we processed the whole database.
  • We had a requirement to build in a verification process, comparing the masked data against the source. This report would answer the question – “have we missed masking any records?”

We created a simple plan to extract the data, load into a SQL database and then mask. Only taking required data increased efficient use of storage and reduced processing time. This would allow the Client’s development team to export the masked data and transfer into the Client Portal. 

Choosing The Right Tool

Identifying the data was a difficult manual process because of the core system’s table/column naming convention. Engaging Data’s Consultant used the WhereScape 3D product, which documented the structure of the system into a metadata layer. The consultant worked with the business teams to update the metadata layer & highlight fields that contained personally identifiable data. In addition, we added business definitions. Using an agile approach, each columns type of data masking requirement was agreed, along with how data joined and stored/reused in different tables. Helpfully, WhereScape 3D provided all the known diagrams and suggested relationships, helping to reduce the investigation time.

At the end of this exercise, WhereScape 3D produced detailed documents of the core systems data structure as well as analysis of the data cardinality/profiles. It uncovered some interesting points about the system, including some parts of the system that held personally identifiable data, that the client had not known existed.

Putting The Data Masking Solution Together

Using the information within the metadata; WhereScape’s Red imported the physical structure of the system and automating the extraction of data into a SQL database on a scheduled basis. We started off daily, but later to increase to every hour.

Now that the data was at rest in the SQL database, our consultant used Redgate’s Data Masker to convert the personally identifiable data to a data set, based on the agreed rules held within the metadata. Once the rules had been designed, WhereScape’s Red scheduler automated the masking so that it started as soon as the loading has completed. 

Data processing, including masking and being loaded into the target database, took place within 4 hours (initially). Not too onerous and very timely compared to other options. More importantly, meant we reduced processing time by a further hour.

Did The Data Masking Work?

Using WhereScape Red, the Engaging Data consultant was able to build a comparison process, that utilised the metadata (only using those field marked as containing personally identifiable data) and compare the values before and after the process. 

The processed ends with an automatic email of the data masking comparison report. This report contains a summary of field error analysis as well as a number of field errors per record. The latter was used to fail the process & prevent the data from be transferred to the target database. Automating this, enabled the Client to feel confident that the process was working correctly.

In Conclusion

All sorts of tools can be used to mask data. We find the best of them will automate the process allowing you to decide how to mask, when to mask & how frequent to do it.  


If you would like to learn more about this Redgate‘s Data Masker, WhereScape Red or how we can help with your data project, please feel free to contact office@engagingdata.co.uk