Enterprise Data Strategy Handbook: SME Solutions Group

A data strategy is not just a plan but a guiding force encompassing people, processes, and technology. Discover how a holistic, long-term approach can reshape your organization's data landscape. A successful data strategy is not a quick fix; it's a journey. By adopting a holistic perspective, organizations can create a resilient foundation addressing current challenges and uncertainties. This approach seamlessly integrates people, processes, and technology, paving the way for a comprehensive and sustainable data strategy.

Determining your data points

Are you aware of the data points your organization engages with at any given point for each process? You may have an idea, but you may be touching the tip of the iceberg unless you're the in-house information technology expert. Most organizations have way more data than they can handle seamlessly, including various factors encompassing business value, data governance, data culture, and architecture.

That’s why we want to start with what kind of data points to consider when considering your enterprise data strategy. The first step is determining what use cases will be the most impactful for your business's bottom line. Investing in a complete data strategy can be pricey, and getting started by understanding your data landscape will help when the times come to determine what technology you will want to start with and inform your eventual roadmap. Consider how your available data can help your organization gain a competitive advantage by optimizing operations, improving the customer experience, and even with product development or improvement.

Then, you’ll want to consider your data strategy. This involves deciding how you want your data initiatives to be structured, including defining key stakeholder roles and responsibilities, standard operating procedures, and coordination mechanisms for optimal data handling. Clear guidelines will help mitigate risks, ensure compliance, and protect your organization’s reputation by avoiding potential breaches of data privacy or integrity.

Of course, when evaluating your existing data and the available infrastructure, you can map out how your data strategy can help achieve your overall strategic objectives. This involves evaluating the existing infrastructure, identifying gaps and inefficiencies, and selecting the most suitable tools and technologies to support data-driven initiatives. 

By designing a scalable and flexible architecture that accommodates evolving business needs and technological advancements, companies can future-proof their data infrastructure and ensure its alignment with strategic goals. In essence, having answers to these critical questions enables companies to formulate a data-oriented strategy that is not only aligned with business objectives but also equipped to drive meaningful and sustainable outcomes through data-driven decision-making.

So, now that we’ve established a data strategy, let’s understand why having a plan is so important. We work with companies and organizations with access to multiple data sets used constantly. By the time they come to us, it’s usually because the management has become overwhelming or there are clear inefficiencies. Without a strategy to consolidate data to and make it valuable and accessible to all parts of the organization, all that data is essentially useless. It can take someone hours to pore multiple data sets to glean valuable business insights. With the right data strategy in place, we can help ensure you’re getting the best value from your assets, thus improving performance overall.

Alignment with business strategy

Aligning data strategy with business goals ensures that data initiatives contribute directly to the organization's objectives and priorities. One approach to achieve this alignment is by closely collaborating with key stakeholders across different departments to identify and prioritize strategic business goals that data-driven insights can support. By understanding the organization's specific challenges and opportunities, data strategists can tailor data initiatives to address these needs effectively, improving operational efficiency, enhancing customer experiences, or driving revenue growth.

Integrating data strategy discussions into broader business planning processes, such as strategic planning sessions and budgeting cycles, can help reinforce the connection between data initiatives and business goals. This involves articulating the value proposition of data-driven decision-making and demonstrating how data analytics can inform strategic decision-making, mitigate risks, and capitalize on opportunities. By framing data strategy discussions regarding their potential impact on key performance indicators (KPIs) and bottom-line outcomes, data strategists can garner support from business leaders and secure buy-in for data initiatives.

To gain business buy-in and executive support for data initiatives, it's essential to communicate the value proposition of data-driven decision-making in terms that resonate with business leaders' priorities and concerns. This may involve highlighting tangible benefits such as cost savings, revenue growth, or competitive advantage and showcasing success stories and case studies from similar organizations that have achieved meaningful outcomes through data-driven strategies. Additionally, emphasizing the importance of data governance, privacy, and security can help alleviate data risks and compliance concerns, fostering trust and confidence in data initiatives among business leaders.

Engaging in ongoing dialogue and collaboration with business stakeholders can help ensure data initiatives align with evolving business priorities and objectives. This involves soliciting feedback, addressing concerns, and adapting data strategies in response to changing business needs and market dynamics. By fostering a culture of partnership and collaboration between data teams and business units, organizations can foster a shared understanding of the value of data-driven decision-making and drive collective efforts toward achieving strategic business goals. Ultimately, gaining business buy-in and executive support for data initiatives requires a strategic approach that emphasizes alignment with business priorities, articulates the value proposition of data-driven decision-making, and fosters collaboration and engagement across the organization.

Analytics and Data Maturity Evaluation

So, now that we’ve established the importance of a data strategy, how do you get started? Beginning with a business intelligence assessment is a great place to start. Assessing current analytics maturity is crucial for organizations seeking to enhance their data-driven capabilities and maximize the value of data analytics initiatives. Understanding the current state of analytics maturity provides valuable insights into strengths, weaknesses, and areas for improvement, enabling organizations to develop targeted strategies and allocate resources effectively to advance their analytics capabilities.

One key reason for assessing analytics maturity is to identify gaps and opportunities for improvement in various aspects of the analytics process, including data collection, analysis, interpretation, and decision-making. By assessing current maturity levels across these dimensions, organizations can prioritize investments in areas with the greatest potential to drive business impact and achieve strategic objectives.

Evaluating analytics maturity allows organizations to benchmark their performance against industry standards and best practices, providing context and perspective on their relative position within the competitive landscape. This benchmarking process helps organizations set realistic goals and expectations for their analytics initiatives and identify leading practices and success factors that can inform their improvement efforts.

Conventional cloud BI solutions offer sleek user interfaces yet require users to manage data arduously. Simply put, they streamline interaction with data but must address data management. Consequently, users are left responsible for extracting, organizing, and maintaining data within a data warehouse. What's truly required is a comprehensive service that manages data from start to finish. Hence, organizations are increasingly opting for BI as a Service solution.

Enterprise industry software: build vs. buy

Implementing the most effective software is imperative when developing your data strategy. Whether you adopt a specific brand-name software solution or prefer a fully updated tech stack, SME Solutions Group can help guide you through decision-making. We engage in ongoing technology “bake-offs” to compare and contrast the latest software solutions and their updates to ensure we provide our customers with the best possible technology available, depending on their needs. 

Another consideration is whether your organization wants to build a proprietary solution from the ground up or buy a series of existing solutions that can be automatically integrated for ease of use. In either case, SME Solutions Group can help assess your needs. Depending on the industry and enterprise size, it makes sense to build something in-house. However, the time and cost is usually somewhat higher, so we try to start with what we know works and then go from there.

Fundamentally, SME Solutions Group’s technology vetting process focuses on minimizing risks and optimizing the likelihood of success for specific technology projects or initiatives. This entails conducting a comprehensive assessment to determine their appropriateness and excellence for the intended objectives. 

A few that we recommend include:

1. Snowflake
2. Amazon Web Services
3. DataOps.live
4. data.world
5. DBT
6. Fivetran
7. H2O.ai
8. Matillion
9. Microsoft
10. ThoughtSpot
11. Qlik
12. Sigma

When coupled with appropriate training and support, these software solutions are crucial in making data analytics accessible across organizations. It’s more than just getting data; it's also about how that data can influence decision-making processes and overall business insights. Businesses can leverage these tools to effectively address data challenges and align their efforts with overarching business goals. With data-backed decisions, enterprises can respond responsibly to market changes and address their stakeholders if difficult changes occur or are required. 

For instance, Snowflake offers a cloud-based data storage and analytics service, generally termed "data-as-a-service". Similarly, platforms like data.world and ThoughtSpot provide intuitive interfaces and artificial intelligence capabilities, empowering users to derive valuable insights from their data without extensive technical expertise.

Moreover, solutions like Fivetran facilitate seamless data integration and pipeline management, streamlining the process of collecting and preparing data for analysis. This enhances operational efficiency and enables businesses to make more informed decisions promptly.

Adopting these software solutions is instrumental in overcoming data-related challenges and driving business success by enabling data accessibility, insight generation, and alignment with organizational objectives. However, there is always room to improve on the existing options with in-house software! It really depends on your organization’s needs, and that’s where SME Solutions Group can help.

Data Architecture and Technology

As we’ve stressed above, it's crucial to collaborate with the appropriate team to sidestep technology selection pitfalls. Additionally, emphasis should be placed on seamlessly integrating tools into the overarching data architecture.

Effective integrations enable different tools to work harmoniously, facilitating data flow across systems and processes. For instance, integrating data analytics tools with data storage platforms allows for real-time analysis of large datasets without manual data transfers. Similarly, integrating data visualization tools with business intelligence platforms enables stakeholders to gain insights from data visualizations generated directly from underlying datasets.

Integrations help streamline workflows and automate repetitive tasks, enhancing productivity and reducing manual effort. For example, integrating data ingestion tools with data quality and cleansing tools automates the process of cleansing and standardizing incoming data, ensuring that it is accurate and consistent before being used for analysis or reporting. Similarly, integrating customer relationship management (CRM) systems with marketing automation platforms enables personalized marketing campaigns based on customer behavior and preferences captured in the CRM database.

In modern analytics team structures, organizations can adopt operating models tailored to their specific needs. These models typically include decentralized, centralized, and hybrid approaches.

In a decentralized model, data analytics teams are distributed across different departments or business units, allowing for closer alignment with unique business needs and objectives. This structure promotes autonomy and agility, enabling teams to respond quickly to emerging data challenges and opportunities within their respective domains.

Conversely, in a centralized model, data analytics functions are consolidated under a single, centralized team or department. This approach fosters consistency, standardization, and economies of scale in data management and analytics activities. By centralizing expertise and resources, organizations can streamline processes, reduce duplication of efforts, and ensure compliance.

A hybrid model combines elements of both decentralized and centralized structures, allowing organizations to leverage the benefits of both approaches. For example, a hybrid model may involve a centralized data team responsible for establishing overarching data architecture and governance frameworks while also empowering decentralized analytics teams embedded within business units to address specific analytical needs and drive innovation at the grassroots level.

Organizations must assess and fill skill gaps within their data analytics teams regardless of the chosen operating model. This involves identifying the core competencies and expertise required to support data-driven decision-making and strategic objectives. By investing in training, recruitment, and professional development initiatives, organizations can ensure that their data analytics teams possess the technical skills, domain knowledge, and analytical capabilities to deliver value and drive business success.

One way we at SME Solutions Group like to think of a good model is what we call the hub and spoke model. This model stems from your data as your central source of truth, surrounded by the IT team, and then reaching out into a shared data model that allows various teams across your organization to access the needed data when needed. No matter what model you decide makes the most sense for your organization, BI implementation programs without a data engagement model will never achieve optimal efficiency, maturity, or return on investment.

Data Governance

SME Solutions Group employs a range of best practices regarding data governance, ensuring that organizations can effectively manage and utilize their data assets. One key aspect of these best practices involves tailoring data governance strategies to meet each organization's unique needs and challenges. 

This customization involves understanding the client organization's specific business objectives, industry regulations, organizational culture, and IT infrastructure. By taking a tailored approach, SME Solutions Group can design effective, practical, and sustainable data governance frameworks in the context of the organization's operations and goals.

SME Solutions Group emphasizes the importance of collaboration and stakeholder engagement in developing and implementing data governance frameworks. This involves working closely with key stakeholders across different departments and organizational levels to ensure buy-in, alignment, and ownership of the data governance initiatives. By involving stakeholders early in the process and soliciting their input and feedback, SME Solutions Group can help organizations foster a culture of data stewardship and accountability, leading to greater adoption and success of the data governance framework.

Finally, SME Solutions Group advocates for an agile data governance approach, emphasizing flexibility and adaptability in responding to changing business needs and technological advancements. This agile mindset allows organizations to evolve their data governance practices over time, adapting to new challenges, opportunities, and regulatory requirements. By adopting an agile approach, organizations can stay ahead of the curve and ensure that their data governance efforts remain relevant and effective in an ever-changing landscape.

After all, data governance ensures that your data is your single version of the truth. With proper data governance, trust can be recovered, which can be much more costly than one might think. It is much cheaper, easier, and more comfortable to rebuild a product or tech stack than it is to rebuild trust within and without your organization.

Change management 

Change management is essential for successfully implementing data security measures within an organization. It involves managing the transition process effectively to minimize disruptions and ensure that new security protocols are adopted seamlessly. This includes:

• Communicating the importance of data security to employees at all levels.
• Addressing potential resistance to new security measures.
• Providing support and resources to facilitate the transition.
• Monitoring progress to ensure effective implementation.
  
  

Here are some of the facts:

• 70% of change initiatives fail. 
• Only 1/3 of major change initiatives fully meet the goals set by the organization.
• 50% of leaders don’t know whether recent organizational changes have succeeded.
• 74% of leaders say they involved employees in creating a change strategy, but only 42% of employees feel included.

Those are the starting points that can seem scary. It is clear that change management is nebulous, and it can certainly throw a wrench into your data management planning and organization. With leadership disconnected and failing initiatives, all you’ve invested in your data strategy could be useless if not handled properly. Don’t worry, SME Solutions Group is here to help – not only before a significant change initiative but even after a botched attempt. We can help with managing trauma during and after a major transformation initiative.

SME Solutions Group excels in guiding organizations through change management processes, particularly when implementing new data tools. Our expertise lies in understanding the intricacies of organizational dynamics and effectively managing transitions to minimize disruptions. 

By employing proven change management methodologies, we facilitate the adoption of new data tools seamlessly within an organization's culture and structure. SME Solutions Group collaborates closely with leadership and employees at all levels, ensuring clear communication of objectives and fostering a sense of ownership and commitment to the change process. 

Through comprehensive training programs, we work to equip employees with the necessary skills and knowledge to embrace any new tools confidently.Our continuous support and monitoring mechanisms enable organizations to navigate potential challenges and adjustments effectively, ultimately driving successful outcomes in data tool implementation initiatives when engaging in organizational change.

Data security: the importance of training

Apart from helping with change management and technological tool implementation, SME Solutions Group’s consulting arm can help train employees to use new technologies to avoid any breaches that may result in loss of trust. We can’t emphasize enough the importance of proper training, especially during organizational change. People are ultimately the most vulnerable part of any network and, usually without meaning to, are the culprits for hacks or breaches.

Emphasizing training is crucial for ensuring that employees have the knowledge and skills required to maintain data security effectively. This includes providing comprehensive training programs on cybersecurity best practices data protection protocols, and recognizing and responding to security threats. Training should be ongoing and tailored to different roles and levels of responsibility within the organization, ensuring that employees understand their specific obligations and how to fulfill them. Moreover, training should be reinforced with regular updates and reminders to keep security at the forefront of all employees' minds.

Similarly, budget support is essential for allocating resources to implement and maintain robust data security and training measures. These measures include investing in state-of-the-art security technologies, hiring qualified cybersecurity professionals, and conducting regular security audits and assessments. Adequate budget support ensures organizations have the tools and resources to protect their data effectively and respond quickly to emerging threats.

Effective communication is also critical for ensuring that employees understand the importance of data security and their role in maintaining it. This includes communicating security policies and procedures clearly and consistently, providing regular updates on security initiatives and developments, and fostering a culture of transparency and accountability around data security. Organizations can strengthen their cybersecurity posture by emphasizing training, budget support, and effective communication and minimizing the risk of data breaches and other security incidents.

Now that we’ve established a data strategy, the importance of implementing an effective strategy, developing an effective team, and the dangers of cybersecurity breaches, let’s get practical! 

Working with a partner like SME Solutions Group involves an iterative process that helps determine your organization's current status, needs, and solution options that could best meet your goals. When developing a roadmap, we have a specific checklist to help you get started. Then, if you have questions, we’re here to help with technical development and implementation! 

Let’s break it down:

• • Identify the key business objectives and goals the data strategy aims to support. These objectives should align with your organization's overall strategic priorities.
  
  2. Assess Current State:
  • Conduct a thorough assessment of the organization's current data landscape, including data sources, quality, governance practices, and analytics capabilities. This assessment helps identify strengths, weaknesses, opportunities, and threats related to data management and utilization.
  
  3. Set Strategic Priorities:
  • Prioritize the areas where improvements or enhancements are needed based on the assessment. These strategic priorities should address critical gaps or challenges identified during the assessment phase.
  
  4. Define YOUR Data Framework:
  • Establish a data framework that outlines the policies, processes, roles, and responsibilities for managing data throughout its lifecycle as it makes sense for your organization. This framework ensures that data is accurate, consistent, secure, and compliant with regulatory requirements.
  
  5. Develop Data Architecture:
  • Design a data architecture that supports your organization's data strategy and business objectives. This includes defining data models, standards, and protocols for data integration, storage, and access.
  
  6. Implement Data Management Practices:
  • Implement data management practices, such as data profiling, cleansing, enrichment, and standardization, to improve the quality and reliability of data assets. These practices ensure that the data is accurate, complete, and consistent across different systems and applications.
  
  7. Enable Data Analytics:
  • Develop analytics capabilities that enable the organization to derive actionable insights from data. This includes implementing tools, technologies, and processes for data analysis, visualization, and reporting. Work with a professional team to ensure you’re using the right tools to get the best results for value!
  
  8. Ensure Data Security and Compliance:
  • Implement measures to protect data against security threats and ensure compliance with relevant regulations and standards. This includes establishing data security protocols, access controls, and encryption mechanisms.
  
  9. Monitor and Measure Performance:
  • Establish key performance indicators (KPIs) and metrics to monitor the data strategy's performance and effectiveness. Track progress against these metrics regularly and adjust the strategy as needed to achieve desired outcomes.
• 10. Monitor and Measure Performance:
  • Foster a culture of continuous improvement by soliciting feedback, learning from experiences, and adapting the data strategy in response to changing business needs and technological advancements.

By following these steps and working with SME Solutions Group, we’ll help you develop a data strategy roadmap that aligns with your business objectives, enhances data management capabilities, and drives better decision-making and outcomes through data-driven insights.