How do we go about implementing an effective data strategy?

Every organization is different. Every business domain (healthcare, financial, manufacturing, e-commerce, etc.) brings its own unique sets of data challenges (security, privacy, interoperability, compliance, structured data, unstructured data processing, binary data processing, real time data processing, micro-batch data processing, etc.). 

For example, healthcare and financial industries are highly governed and are usually more defensive when it comes to applying AI and ML on patient or customer data. Alternatively, a technology company or a similar fast-moving organization, will generally tend to be more offensive when it comes to implementing a data strategy. 

Healthcare organizations or financial institutions are also likely to deal with highly sensitive data where data privacy and security are paramount. A defensive approach that prioritizes security, access, governance, and accuracy may be safer than, for example, gaining insights using AI and ML for decision making against customer or patient data. It’s possible that getting real-time data and quick insights is likely not a top priority for these industries, whereas providing guardrails for who can access what data, probably is. 

On the flip side, a tech company, an industry that tends to move quickly and relies more heavily on a quick turnaround of data insights, may lean more on offense when it comes to utilizing advanced analytics on data. With that said, there are certainly departments within tech companies, and other fast-moving industries, that will focus more on defense, such as the finance department.

While the industry and sectors are important, equally important is the size of your organization. The size directly ties to whether you will take a centralized or distributed approach to implementing your data strategy. 

In a small company, it’s usually preferable to have a centralized approach so you can focus on harmonizing tools, technologies and processes through a central data and analytics team. As the company size increases, you may need to reconsider the centralized approach, instead opting for a decentralized and distributed approach to avoid bottlenecks created by a central data and analytics team.

The point here is that once you understand which framework (offense, defense, or a combination of both) and which implementation model (centralized or distributed or a combination) works best for your organization without stifling innovation, you can come up with an effective implementation strategy that is appropriate for you and your organization’s business needs.

A few practical tips from the trenches as to how to proceed on data strategy implementation:

While there is no one-size-fits-all, there are some must-haves that you must get right early in the game to be successful at implementing your data strategy. These are generally irrespective of your business domain or size of your organization.

‍Determine your organization’s current data maturity level – before you start embarking on a massive enterprise data strategy initiative, ensure that you perform a maturity assessment first. What’s most important in this exercise is to be realistic as to where your organization falls within the data maturity spectrum. Some example maturity levels are; data aware, data proficient, data savvy, data driven, etc. The best place to begin is to start by asking key people in your organization about the data challenges they are facing and how they are currently tackling those challenges.

Align your data strategy vision and execution with your business strategy and goals – make sure your data strategy is aligned with your business strategy, goals, and associated KPIs. Data strategy cannot work in a vacuum. It is intended to be an enabler, not something that is standalone.

Choose the right data strategy framework and deployment model – as discussed above, choose defense vs offense, or a combination of both. Choose centralized vs distributed, or a combination of both. Again, this depends on your organization’s domain and size.

Select and implement the right data and information architecture – make sure your data architecture is modern, scalable, and highly performant. It should be able to handle data volume, velocity, variety, and veracity. You should also consider whether the architecture needs to support batch as well as near real-time / real-time data processing.

• Invest in a modern cloud-based data and AI platform – make sure your data strategy solves modern AI-driven data product needs. The platform architecture must scale. 
• Implement data security, privacy, and regulatory compliance by design – ensure that data is secured at rest and in motion. Also ensure data sensitivity is considered based on compliance and regulation requirements (GDPR, HIPAA, CCP, among others).
• Choose the right tools, technologies and cloud – ensure your toolsets meet the needs of your use cases. Let the tools and technologies NOT look for problems to solve, instead let your use cases drive the selection of tools. 
• Define roles and responsibilities and build modern skillsets – invest in your team; cloud, AI, ML, data engineering, full stack engineering, automation, MLOps, DevOps and DataOps tools, technologies, and processes are all critical.

Embrace data governance – establish data governance early in the cycle. This is directly tied to data quality and data ownership.

Implement change management – ensure clear communication regarding process changes, governance and ownership structure to avoid any surprises. A successful data strategy requires process changes; it’s not just about technology, cloud and the AI.

Be realistic and practical – don’t try to boil the ocean. You need to win hearts and minds during the implementation so more people in your organization will appreciate the value of your data and digital transformation. Plan for what can be delivered successfully. Use an incremental and iterative approach and demonstrate value early and often.

Reassess regularly – no matter where you are in regards to data maturity, you need to continually assess your progress. Your data strategy will need on-going tweaking. Two areas to look for here are; 1) if there is frustration growing in terms of how long it is taking to get things done, and 2) users are losing trust in the data. A good rule of thumb is to review your strategy every 6-12 months by talking to your users, stakeholders, and customers.

‍Key takeaways:

• The aim of the data strategy is not just to have best management of data. It is to enable data driven business insights that help to solve important business problems, increase competitiveness and drive innovation
• Alignment with your business strategy is critical
• Data governance is one of the key components of a data strategy as it ties back to data quality, security, audit, and compliance
• Change management must be instituted, and it must be communicated early and often
• Architecture, tools and technology choices must solve your business problems at scale
• Build skill sets around cloud, modern data architecture and AI
• To scale, one must employ MLOps, DevOps and DataOps as part of your overall data, cloud, and AI transformation strategy
• Be thoughtful and flexible before selecting your deployment model – centralized,  distributed or hybrid
• Be realistic and results driven. Don’t boil the ocean. Take an incremental and iterative approach, and demonstrate value early and often

In part 3, we will discuss “An Effective Enterprise Data Strategy: Part 3 - the role of data governance.” We’ll talk about implementing an effective data strategy with data security, privacy, and regulatory compliance in mind, having GDPR, CCPA and HIPAA as a general backdrop. Your data strategy implementation must tackle these regulatory challenges before applying AI and ML on top of business data, and establishing data governance and data management best practices early in the cycle are keys to success.

Is this something that interests you? Want to learn more? Let’s connect to discuss how we can help as we have a ton of experience in this space. Please feel free to send us a note at info@aritex.io and we can find some time to have a conversation.

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