Learn to love your data
Business Data Analyst
Are you interested in data? You should be!
If your company doesn’t have effective data management, then it can lead to critical issues that can have a huge negative impact on business. From experience, these are some of the data pain points I have come across:
Important business information being inaccurate
Incorrect reporting, metrics, and MI
Inefficient internal processes
Systems being unable to function correctly
Staff inefficiencies due to dealing with data problems
Not being compliant with industry regulations, resulting in fines
Huge cost implications to the company
These are just some of the outcomes businesses can experience by not managing their data correctly. My aim is to help you to learn to love your data and show you a few ways that you can start to make an impact on your data management.
A bit about me
With over 27 years’ experience within Financial Services, including 17 years at FTSE 100 Hargreaves Lansdown (HL), where I worked within a variety of areas including Company Finance, Business Intelligence, Client Insight & Data and Data Governance, I have now moved to Navos Technologies to help businesses understand why their data is important. In a world where data is literally everywhere (both visible and hidden), we can help you to use that data more effectively.
If someone asked me if I were passionate about data, I would have to say yes. Over the years I have gathered vast data knowledge and understanding enabling me to identify issues and recommend solutions, with the use of analytical skills combined with problem-solving capabilities. By working alongside Database teams, Enterprise Architects, Solutions Architects and Business Intelligence Developers, I have gained a great understanding of underlying data structures.
In addition to my career at HL I have also worked at other IFA firms; however, I didn’t really start to appreciate the amount of data that companies held on a day-to-day basis until I started working at HL. Having the right tools and access really got me thinking about what hidden information and patterns are embedded within data silos, and the best way to tackle them to detect issues before they get reported on. However, to do this you must make sure your data is correct.
Data is like garbage. You’d better know what you are going to do with it before you collect it.
Helping your business
As all Data Analyst’s know, the focus of analysis and problem solving relates to data, types of data, and relationships among data elements within a business system or IT system.
The reason that data collection is important to your business is that it will help you report on critical business metrics, enable business process and increase efficiency, whilst helping you to reduce the level of uncertainty. Most companies should have short and long-term strategies in place when it comes to data. With good data collection and analysis you will always be able to have confidence in your data and put resources where they are needed most. Understanding what areas currently need to be prioritised is essential in helping your business grow and move forward - putting trust back into your data. Areas of importance include your data’s lifecycle, data quality and data management.
The goal is to turn data into information and information into insight
Carly Fiorina, Former Chief Executive Officer, Hewlett Packard.
The data lifecycle
Understanding your data lifecycle is important to help identify the different stages the data will go through, from design and collection to archival and destruction. The flow of data is not always sequential so you may need to return to previous stages to fix data quality issues.
Anyone handing data in your business, from initial collection to eventual output should understand the data lifecycle.
It's good practice to clearly document the data quality at every stage:
The actual data lifecycle will be specific to the organisation and its processes. However, the above process above is a great starting point for most organisations.
Plan - Good planning can prevent problems in data quality before they occur.
Collect, Acquire, Ingest - You will require data based on user’s needs. You will be able to improve the quality at source through rules and metadata.
Prepare, store and maintain - Staged data is prepared for storage, formatted for use at further stages in the data lifecycle and supported for use within the organisation.
Use and process - Use of the data is available for specific business needs like data analysis or production of outputs.
Share and publish - Data is shared internally or externally; data should be quality assured and well documented.
Archive or destroy - When the data is no longer active, having the conversation with the data owner should decide whether it should be archived (available and secure) or destroyed.
An increasing number of businesses are now coming to terms with the fact that managing data quality is a never-ending process. Whether you are an established company, or a start-up that has got all the processes in place to handle today’s data quality problems, you need to understand that there will be new and different challenges tomorrow which will need to be managed to ensure that data is trustworthy.
Data quality dimensions will help your business assess your data and decide if it’s fit for use.
The six standard data quality dimensions used to measure data quality are:
Database Management isn’t simply one approach, but a series of actions (and for some companies, a dedicated system), that controls their business data during the lifecycle process. As your data grows, database management is a necessity in helping you to prevent poor application performance and reduces any wider impact to your organisation compliance and continuity.
With the proliferation of data showing no signs of slowing down, businesses should be investing in database management tasks, database managers, and database management systems to do the following:
Keep your business operations running as planned through the lifecycle
Keep track of customer and employee data inventory
Maintain the ongoing process of application and database performance
Store and organize unique, varied types of data over multiple databases
Automate database processes and procedures
On a personal note, I have found that learning to love and look after your data helps improve quality of working life. This in turn will help your colleagues, and not forgetting your customers, to have a smooth experience working with your company. By understanding the importance of data collection, it will help eliminate some of the frustrations and complications we tackle day to day. Not to mention the incredible business insight data can provide.
These are just some of the actions you can take to improve the management of your data. As well as this, you should also be considering your data architecture which is another topic in itself. Do you need guidance on implementing a data solution that is appropriate for the size and type of business you are in?
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