Discover what Funded Microsoft workshops, assessments and funding you qualify for!

Ask a Question
close Back

Ask us anything

We’ve got experts on hand to help you with any query you might have

This field is for validation purposes and should be left unchanged.

Five considerations for creating an effective data strategy

In today’s data-driven world, businesses must harness the power of their data effectively to stay competitive, innovative, and one step ahead. Data is your organisation’s most valuable asset, and it can unlock new opportunities, create efficiencies, and allow you to create continual operational improvements… but only when it is used, and managed, effectively.

The only way to do that is to create and enforce a robust, comprehensive data strategy – only then will you unlock its full potential, and drive success for your team.

But how do you get there? There are five considerations that are critical to the creation of an effective data strategy.

Modern Analytics Platform

A modern, cloud-native analytics platform enables you to collect, collate and clean your data so that it can be unpicked and analysed to, ultimately, drive smarter decision making.

A modern analytics platform forms the backbone of any successful data strategy. It brings together various data sources, tools, and technologies to enable seamless data processing, analysis, and visualisation. When you go about building a modern analytics platform for your business, there are a few Azure technologies you can leverage.

Azure Synapse, which brings together Enterprise SQL data warehousing and big data services.

Azure Databricks, a fast, easy, and collaborative Apache Spark-based big data analytics service which is designed for data science and data engineering.

Microsoft Fabric, which launched in May 2023, is an AI-powered analytics platform that unites your data and an array of services, enabling you to get more vale from your data than ever before.

Leveraging these will enable you to future-proof your infrastructure and ensure you have the required agility when handling evolving data requirements.

Data Governance and Security

Data governance and security are incredibly important in safeguarding sensitive information and ensuring regulatory compliance. By establishing robust policies, procedures, and controls to govern data access, usage, and sharing across your organisation, you can have confidence in your data’s quality, accessibility, and effectiveness. Implement encryption, authentication, and access controls to protect data from unauthorised access and cyber threats and conduct regular audits and compliance assessments to help maintain data integrity and mitigate risks which, in turn, will foster trust among stakeholders and customers.

When looking to improve your data governance and security, it’s important you consider:

  • Ownership of data – who is responsible for your data and its management?
  • Data catalogue – where, and how, is your data stored?
  • Data lineage – can you track your data’s journey and trace it back to source?
  • Data classification – is your data organised effectively, and therefore accessible and searchable?

Data Quality

Data quality is the foundation of reliable insights and informed decision-making. Investing in data quality management processes, to identify and rectify inaccuracies, inconsistencies, and incompleteness in your datasets, is the only way to guarantee your decisions are informed correctly. You can also leverage data profiling, cleansing, and validation techniques to ensure data accuracy, completeness, consistency, and timeliness.

By prioritising data quality, you can enhance the credibility and usability of your analytics outputs to drive better business outcomes and fuel strategic initiatives. When you have data that you can trust, you and your team will gain confidence in the business decisions you are making off the back of those data insights – which should ultimately drive the business forward.

Data Modelling

Dashboards serve as visual representations of key performance indicators (KPIs) and make the insights derived from data analysis much more digestible. To maximise the effectiveness of your dashboards, there are five data models worth considering:

  • Descriptive: Summarise historical data trends and patterns to provide context and understanding of past performance.
  • Real-time: Monitor and visualise data in real-time to enable immediate action and response to changing conditions.
  • Diagnostic: Identify root causes and correlations within data to troubleshoot issues and optimise processes.
  • Predictive: Forecast future trends and outcomes based on historical data patterns and predictive analytics models.
  • Prescriptive: Recommend actionable insights and strategies to achieve desired outcomes and address business challenges effectively.

Each data model has its purpose, and applying the correct one will empower stakeholders with actionable insights at various levels of the organisation, facilitating data-driven decision-making and performance monitoring. This is where tools like Microsoft Power BI really come into their own.

Generative AI

Generative AI, powered by advanced machine learning algorithms, enables the creation of new, synthetic data based on existing datasets. By leveraging generative AI techniques such as generative adversarial networks and variational auto-encoders, businesses can augment their existing data assets and generate synthetic data for training AI models. This not only addresses data scarcity and privacy concerns, but also enhances model performance and generalisation capabilities. Whether it’s generating realistic images, text, or other data types, generative AI opens up new possibilities for innovation and experimentation in any number of ways.

The main concern that businesses face is how to responsibly manage AI. Microsoft is one of the industry leaders in empowering businesses to leverage AI responsibly and have been investing billions of dollars into their technology to ensure businesses are equipped to do so. They’ve produced guideline on how to empower responsible AI practices, which is well worth the read.

 

Building a successful data strategy requires careful consideration of a wide range of factors, from infrastructure and governance to quality, analytics, and AI, but by prioritising these considerations and embracing emerging technologies and best practices, businesses can unlock the full potential of their datasets and drive sustainable growth – all while building a competitive advantage over others the market.

To help you understand how you can unlock the power of your data, we are running fully-funded Data & AI workshops to apply these learnings to your specific business context.

Sound good? Let’s talk.