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Data Driven on AWS

Being ‘data driven’ is driving us nuts. Why? It may well be an overhyped term used by marketing people (cough, guilty) but let’s break it down. A data driven approach is an evidence-based approach; it’s as Jeff Bezos put it, “The most junior person in the room should be able to overrule the most senior if they have the data!”

Being 'data-driven’ refers to the practice of basing decisions on the analysis of data, rather than purely on intuition or personal experience. In a data-driven approach, data is collected, analysed, and interpreted to guide strategic decisions, operational improvements, and innovations. This method leverages statistical analysis, data mining, and predictive analytics to extract insights that inform decision-making processes. None of that empirical goodness can be surfaced if the data is garbage, the pipelines are wonky and the culture of your business does not value the importance of using data to inform good judgement.

We had, yet again, some incredible panelists joining us to bring this topic to life:

Watch the on-demand event video:

What is “data driven” decision making?

Making decisions based on hard data can mean the difference between success and failure. The term ‘data-driven decision-making’ is really about embracing the ability to identify trends, understand customer behaviour, optimise operations, and predict future outcomes with greater accuracy. In our corner of the industry, as practitioners, we have to use data to inform our decisions because the metrics we set are measurements of our achievement.

How does this fit with being on AWS?

The relationship between being a data-driven culture and cloud platforms like AWS (Amazon Web Services) is significant in the context of enterprise data management. There is no point in having access to 1000+ world class web services and not having a culture that values the way of thinking that comes with being agile, embraces the cloud, wants new insights, etc.

AWS provides a robust infrastructure for storing, processing, and analysing massive volumes of data. Its wide array of services, from data warehousing with Amazon Redshift to real-time analytics with Amazon Kinesis, means that implementing scalable data-driven strategies is no longer this unattainable outcome where cost is the barrier. 

AWS also offers advanced machine learning and artificial intelligence services, which are crucial for developing predictive models and gaining deeper insights from data. If you’re catching up on this guide for the first time, be sure to sign up for our April 2024 event on GenAI!

How can your business become more data-driven on AWS?

Step 1: Establish a Clear Data Strategy: A comprehensive data strategy should define how data will be collected, stored, managed, and used across the organisation. This involves setting clear goals for data usage, identifying key performance indicators (KPIs), and ensuring that data practices align with the overall business objectives. Establishing data governance policies is also crucial to maintain data quality and security.

Step 2: Invest in the Right Technology and Tools: Leveraging the appropriate technology is essential for effective data management and analysis. This includes investing in robust data storage solutions, advanced analytics platforms, and tools that support data visualisation and business intelligence. Cloud platforms like AWS offer scalable and flexible services that can support a data-driven infrastructure, from data warehousing to machine learning capabilities.

Step 3: Foster a Data-Driven Culture: Cultivating a culture that values and utilises data in decision-making processes is critical. This involves promoting data literacy across the organisation, encouraging employees to use data in their daily tasks, and recognising and rewarding data-driven achievements. Leadership should lead by example, demonstrating a commitment to data-driven principles in strategic decisions.

Step 4: Democratise Data Access: Making data accessible to employees across different departments can empower them to make informed decisions and contribute to data-driven initiatives. Implementing self-service analytics tools and providing training can enable non-technical staff to analyse data and gain insights relevant to their roles. However, it's important to balance accessibility with data security and privacy considerations.

Step 5: Leverage Data Analytics and Insights: Businesses should actively apply data analytics to gain actionable insights. This involves not just collecting and storing data, but also analysing it to uncover trends, patterns, and opportunities. Advanced analytics, predictive modelling, and machine learning can provide deeper insights that guide strategic planning, operational improvements, and innovation.

What changes do we see in the market?

We’re spotting a few trends at Cloudsoft. Firstly, the rise of artificial intelligence and machine learning is enabling extremely sophisticated analysis and prediction capabilities. Unfortunately, the foundations for data quality and model ops are often insufficiently mature to really get value out of these technologies. We’ll dive into that in a bit. Secondly, the increasing adoption of data democratisation within organisations is empowering more employees to access - a wonderfully terrifying thought - and utilise data in their decision-making, leading to a more informed workforce. Thirdly, the integration of real-time analytics is becoming more prevalent. Dashboards over spreadsheets, automated reports over paper memos… the change is happening.

Don’t forget about the Data Engineers!

Data engineers play a crucial role in helping business teams become more data-driven. They are responsible for designing, building, and managing an organisation's data architecture. In this online event, we had Darren Ko, a solutions architect at AWS, really dig into this role to understand how they are straddling the data consumers vs producers divide.

DEs have a role in ensuring data quality (as much as they’d rather push that back onto the data producer but that rarely works), developing data pipelines with ETL capabilities, and implementing process that get the data flowing in the right direction. These are the people that allow for the efficient collection, storage, and analysis of data, and AWS is the ideal operating ground because it makes it so easy to succeed. By providing reliable, accessible, and high-quality data, data engineers enable business analysts, decision-makers, and other stakeholders to get insights from data that they can act upon. Top tip: your first hire after delivering a data strategy will be a data engineer, even if that’s not what they are called.

So how can businesses get it right?

Alignment with Business Objectives: Goes without saying that a well-defined operating model ensures that data management practices support the overarching business goals. By aligning data initiatives with strategic objectives, organisations can prioritise data projects that offer the highest value and ensure that data resources are allocated effectively to support growth and innovation.

Efficiency and Scalability: Markets move fast. The right operating model streamlines data processes, eliminates redundancies, and ensures consistency in data handling across the organisation. This efficiency is crucial for scaling data initiatives and managing the increasing volume, variety, and velocity of data in today's digital landscape. 

Role Clarity and Accountability: A clear operating model delineates roles and responsibilities within the data management framework. This clarity ensures accountability, with specific teams or individuals responsible for data quality, compliance, security, and utilisation. It fosters a culture of ownership and accountability, which in turn is critical for maintaining high data standards.

Risk Management and Compliance: The operating model incorporates Data Governance principles that address risk management and compliance requirements. By defining how data is to be handled, stored, and secured, and establishing clear policies for data privacy and regulatory compliance, organisations can mitigate risks associated with data breaches, legal penalties, and reputational damage. Think: GDPR time.

Adaptability and Innovation: An effective operating model is flexible, allowing organisations to adapt to changes in the business environment, technological advancements, and regulatory landscapes. This adaptability is crucial for innovation, as it enables teams and organisations to quickly leverage new data sources, analytics technologies, and methodologies on AWS to really gain competitive advantage.

Operating Model, Governance, Data Quality…

Choosing the right operating model is essential to a successful data strategy because it provides a structured framework that aligns data management practices with the organisation's business objectives, operational needs, and cultural dynamics. 

In essence, an effective operating model determines how data flows within the organisation, how data responsibilities are distributed, and how data governance is applied, ensuring that data assets are leveraged efficiently and effectively. 

Data Governance and Data Quality are pivotal elements in the realm of data management if they want to unlock the potential of their data assets. Data Governance encompasses the overarching policies, standards, practices, and procedures that organisations put in place to manage their data assets. It is a strategic approach to managing data that ensures accountability and clarity over data management roles and responsibilities. It should include:

  • Establishing Clear Data Policies and Standards: Defining how data is collected, stored, accessed, and shared within the organisation to ensure consistency and compliance with legal and regulatory requirements.
  • Data Stewardship: Assigning roles and responsibilities for data management to ensure accountability for the accuracy, privacy, and security of data.
  • Data Compliance: Ensuring that data management practices comply with relevant laws, regulations, and industry standards, such as GDPR for personal data protection in the EU.
  • Data Lifecycle Management: Overseeing the entire lifecycle of data, from creation and acquisition to archiving and disposal, ensuring that data is kept up-to-date and relevant.

Data Quality, on the other hand, refers to the condition of a set of values of qualitative or quantitative variables. High-quality data should be accurate, complete, consistent, relevant, and timely. The role of Data Quality in an organisation includes:

  • Accuracy and Completeness: Ensuring that data accurately reflects the real-world entities or events it represents and that all necessary data is captured.
  • Consistency: Maintaining uniformity in data across different systems and datasets, preventing discrepancies that could lead to erroneous conclusions.
  • Relevance: Ensuring that data is pertinent and applicable to the purposes for which it is used, aligning with the organisation's objectives and decision-making needs.
  • Timeliness: Ensuring that data is up-to-date and available when needed, enabling timely decision-making and operational processes.

Together, Data Governance and Data Quality create a foundation for data-driven decision-making, allowing organisations to trust their data, derive meaningful insights, and ensure compliance with internal and external standards. When the two work well, it reduces risks of poor data management, and fosters a culture that effectively utilises data.

Got some feedback?

As ever, we would be interested to hear about your experience with Data & Analytics on AWS. Please email with your comments and suggestions or book a free session with one of our cloud experts.

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