Knowledge is power: harnessing data for business

We live in a world of big data. Modern organisations cannot afford to miss the opportunities for getting a competitive edge that are provided by good data management and data analytics.

What is data management?

In brief, it’s the collection, organising, protecting and storage of an organisation’s data – everything from sales figures to HR data – so that it can be sliced, diced and analysed to inform business decisions.

The digital transformation of our world, enabled by new technologies, means today’s organisations are creating and consuming data at a faster rate than ever before. We live in the age of big data. As IBM states, the last decade has seen exponential growth of data with the recent emergence of artificial intelligence, the Internet of Things (IOT) and cloud computing. Good data management is essential to navigate this complexity and make sense of it all.

Coming to the rescue are data management platforms and software. Good data management tools can prepare, catalogue, search and govern enterprise data to make sure that business decisions are based on reliable, up to date data sets and that people can quickly find the data they need for analysis.

What are the four key considerations for data management?

Given all of the types of data held by an organisation, data management’s scope is broad. The best data management strategies tend to use the following four components to make the process smooth and efficient.

Processing

Raw data is the product of data collection processes which gather information from all kinds of sources, from web APIs, mobile apps, forms, surveys and more. It is processed or loaded via data integration techniques such as ELT (extract, load, transform) which has the advantage of being able to handle real-time data. Next, the data is filtered, merged or aggregated according to what it is being used for – whether that’s a machine learning algorithm or a dashboard to gain business intelligence.

Storage

How data is stored depends on its type and purpose. Data warehousing, for example, needs a defined structured framework or plan to meet the requirements for data outputs (for example, data visualisations). Business users will work with data engineers to create this. Data lakes are another form of big data storage system, often favoured by data scientists, because they allow both structured and unstructured data to be incorporated into a data science project for analysis.

Governance

This is a set of standards and processes to make sure that data assets are effectively used within a business or organisation. Typically it will include processes around data quality, access, usability and security. For example this could include shared naming systems (taxonomies) to make sure metadata is consistently applied across all the different data sources, along with a data catalog to ensure that users can easily access it. Particularly in light of GDPR, data privacy is a key part of governance and data governance teams should help the business to define roles and responsibilities to make sure that only appropriate access is granted.

Security

As digital technology becomes increasingly integral to our lives, there is more scrutiny than ever on the security of modern businesses to protect customer data from unauthorised access, theft by cybercriminals or corruption.“Data security teams can better secure their data by leveraging encryption and data masking within their data security strategy,” IBM recommends.

Why bother with data management?

Maybe you’ve been getting along fine without one. So does your company really need a data management system? Well, as Sir Francis Bacon wrote back in the 16th century, “knowledge is power” – data management solutions and analytics can optimise business processes and give an organisation important insights that add value for customers and improve its bottom line. “With effective data management, people across an organisation can find and access trusted data for their queries,” says visual analytics platform company, Tableau. Some of the key benefits of data management are:

People can see what’s there

Data management makes a company’s data assets more visible so that people can find the right data for their analysis fast, and with trust and confidence. That translates into efficiency and productivity for the organisation.

Data can be trusted

With the processes and policies it puts in place for data usage, data management minimises errors and builds trust in the data. If a company has reliable, up-to-date data, it can respond more efficiently to market changes and customer needs.

Information is safe and secure

Data management protects your organisation and its employees from data losses, thefts, and breaches with authentication and encryption tools. Data management best practice contributes to strong data security ensuring vital company information is backed up and retrievable should the primary source become unavailable. Additionally, security becomes more and more important if your data contains any personally identifiable information that needs to be carefully managed to comply with consumer protection laws.

Scale it up or down and reuse

If your company’s data is well managed it’s easier to effectively scale or use repeatable processes to keep it up to date, along with its metadata. Easier to repeat data management processes help to avoid the unnecessary costs of duplication, such as employees repeatedly carrying out the same research or re-running the same costly queries needlessly.

What is the difference between data management and data analytics?

While data management is the process of managing, storing, organising and sharing data, data analytics is the process of using computers to analyse large amounts of data to uncover hidden patterns, unknown correlations, and other useful insights.

“Data Management is required as the first step towards Data Analytics, but it is equally important. If there is no Data Management in place then Data Analytics will not be helpful,” explains a piece on VP Data’s website.

What is the role of data analytics in management?

Data analytics can be a powerful decision-making tool for businesses to decide the future of the organisation, and it can improve efficiency and effectiveness with overall time and cost savings.

For example, analytics software automatically compares data from one company or industry to another, or from the same company or industry over time, using algorithms to track patterns.

Analytics can improve outcomes for all types of decisions, from large-scale, to micro-scale, from real time to cyclical and from strategic to operational. It can also discover new questions and solutions, and opportunities that managers might not even have considered.

There’s evidence that data analytics can provide competitive edge: a report from Wegener & Sinha showed that firms using data analytics are twice as likely to be in the top-quartile of financial performance within their industries, three times more likely to execute decisions as intended and five times more likely to make decisions faster. Another report from McKinsey shows that data-analytic firms increase their earnings before tax by 20% compared with those that do not.

What are the common data analytics tools?

There are three categories of data analysis tools available for businesses, depending on what is needed: 

1. Software for big data analysis – Big data analysis involves processing vast amounts of data (a data lake or data warehouse) which requires tools focused on speed and security. Two examples of tools for this purpose are Google’s cloud-hosted data warehouse BigQuery and MATLAB – a programming language coupled with a computing environment that can handle extremely complicated and technical data analysis.
2. Software for product analysis – many products exist that help analyse the way customers interact with products. Some common examples are Google Analytics, which offer insights into your users and easy-to-understand data reports and Amplitude – a product intelligence platform that helps you understand more about the people using your platform by providing user insights, then offers analytics to help you provide a better customer experience.
3. Analytics platforms – for a greater depth of business intelligence, data visualisation, sales funnels and marketing analytics there are full data analytics platforms for businesses. Common examples are the data visualisation platform Tableau and Google’s Looker. 

Learn how to harness the power of data in leadership

Keele University’s MSc Management with Data Analytics is a career-focused MSc degree designed for aspiring, data-led leaders aiming to progress into more senior roles. This 100% online and part-time master’s programme allows you to learn while you earn, developing into a well-rounded, active, thoughtful, reflective and practical professional with an appreciation for the strategic and operational challenges of running an organisation – and how to harness the power of data.

This master’s programme relates theory to real-world practice from the outset. Your teachers will be highly experienced, research-active academics and you’ll collaborate with other postgraduate students from all over the world, giving you the opportunity to expand your global network.

Relating theory to real-world practice from day one, you’ll acquire a range of attributes that will stay with you for the rest of your working life whilst setting you apart from other management graduates. Graduates of the Keele Msc Management with Data Analytics have the ability to evaluate and apply theories to various contemporary management problems, an appreciation of the social, environmental and global implications of management studies, and a cutting-edge knowledge of data analytics.