Data strategy - making your data work for you and the team you need to make it a reality

Data is your most valuable resource. It’s the key to profit.  

While the buzzwords have been plentiful, pinpointing the starting line can be challenging. Today, it’s not just the early adopters who are leveraging data to guide their decisions and gain a competitive edge—it’s becoming standard practice. What sets the true pioneers apart is their commitment to a proactive, comprehensive data strategy. This strategy encompasses the technology, processes, people, and governance needed to manage an organization’s information assets effectively. Those equipped with such foresight are not just participating in the data revolution; they are leading the charge towards machine learning and AI innovations. The question then becomes: how can your enterprise not just join, but lead in this transformative journey? Ensuring the integrity, protection, management, and strategic use of your company’s data through a robust data strategy is the key to unlocking this potential. 

1. Define your objectives

Often led by the Chief Data Officer, a data strategy helps you to outline how you can use your data to achieve your business objectives. So, the first step? Gain clear sight of those objectives. You must connect your business strategy to your data use. Define your priorities and work with leadership to gain a clear understanding of the business problem that you want data to solve – and the obstacles in your path. 

2. Build your team

Crafting a data strategy is a collaborative effort that involves a diverse group of professionals, and we can help you.  

Data engineers and data scientists: These experts build data architectures and models that collect, process, store, and analyze data. They’re the backbone of your data infrastructure. 

Data analysts: Their role is to interpret, analyze, and align data with organizational requirements. They bridge the gap between raw data and actionable insights. 

Stakeholder engagement: Beyond technical roles, engaging the right stakeholders is crucial. Legal, finance, IT, and relevant operational departments should be part of the conversation to ensure alignment and compliance.

At Hays, we’re committed to guiding you through this collaborative process, leveraging the expertise of our team and helping you create a data strategy that drives success 

3. Get great data

Next, you must ensure you are collecting data that is reliable and relevant. With the huge amount of data that we now generate, it is important that the information you need does not get lost in a data silo or hidden in the noise of excessive data. 

Then comes the challenge of quality assurance. High quality data is essential to make technologies like machine learning effective for your business. Many businesses work hard to ensure there are no discrepancies in their data so that it is consistent and reliable. This allows them to reap the full benefits from analytics and reporting. 

Something as trivial as inconsistent spelling can impact marketing campaigns and customer experience. Or data inaccuracies in highly regulated industries like life sciences can result in a lack of transparency in the supply chain and delayed decision making. 

If your goal is to create a data-driven business, you must work hard to remove ambiguous, biased, and duplicate data for better business insights downstream. Data pipelines must be regularly maintained and updated, automating where possible to reduce the introduction of error.

4. In the clouds

Insights gleaned from data are only powerful when in the right hands, so it's important that your data is both secure and accessible. Cloud storage allows businesses to store data at a lower cost with reduced risk. Many organizations use cloud-based platforms like AWS, GCP and Azure to store and manage data. They offer three advantages: they have unlimited capacity, authorized users can access information anywhere at any time, and they allow businesses to increase and decrease storage capacity as needed. In the cloud, your files are securely managed and thanks to machine learning, your unstructured data can be mined for new insights. 

5. Good governance

A robust data governance framework forms a vital part of any data strategy. It looks at the overall approach to collecting, managing, securing, and storing data in the organization. It defines roles, responsibilities, and processes for ensuring accountability for and ownership of data across your business. Good data governance needs teamwork and investment, but it ensures better compliance with data regulations, better risk management and improved trust in data reliability. But there are challenges too -governance structures must be monitored to avoid data becoming overly siloed or overly restricted. 

6. Analyze this!

Without proper useful analysis, data is just a bunch of numbers. Data analytics allow your business to analyze raw data to discover, interpret and communicate meaningful patterns. This helps you to understand consumer behaviors, identify market trends and gain visibility across your supply chain. BI software like Tableau, Salesforce, and Spotfire are used to analyze data and create visualizations that break down complex data insights for stakeholders. You can then use this information to make your business processes more efficient, build strategies, personalize the customer experience, and drive growth. With the wealth of data that is now available, your business can also employ AI driven tools to analyze massive datasets in a fraction of the time it once took.

A culture of success

The aim of a data-driven work culture is to have everyone in your organization working from the same real time, integrated data, armed with a holistic up to date view of the business and vital predictive insights. This means your workers are empowered to make value-driven decisions that consider the interests of your entire organization, and it ensures that your business is agile in the face of certain change.