Building blocks for an effective Data Analytics Organization

Data analytics is the process of analyzing the data using specialized systems and applications to extract relevant, meaningful information to draw insights and identify patterns. It enables organizations to make more-informed business decisions, and scientists and researchers to verify the theories and models.

Data analytics initiatives can help organizations or functions within the organization in a wide variety of ways, for instance, to name a few, improve operational efficiency, increase revenues and (or) profitability, reduce cost, drive improvement of product and services, respond more quickly to fast changing environment and market trends, improve customer satisfaction, identify new opportunities, gain competitive advantage, to address the primary objective of improving business performance.

Data Analytics promises a lot of benefits but successfully deploying analytics to realize the benefits in any organization can be challenging.

The following are the building blocks applied to establish an effective Data Analytics Organization which adds value to the business by providing actionable insights. By understanding and applying them, an organization will be able to create a clear picture of where they are, where to go, why to go and how to get there.

• Business – Analytics Strategy Alignment
• People
• Process
• Technology
• Data Analytics Life cycle

Business – Analytics Strategy Alignment

One of the most persistent issues facing Data Analytics organization in any industry today is the lack of synchronization between analytics initiatives and the business strategic goals. This disconnect has caused failed initiatives, lost opportunities, loss of investment, time and resources leading to loss of business trust and adoption.

Such failures and loss of business trust shows us the importance of collaboration between business and data analytics organization.

What is Strategy alignment?

Business - Analytics (B-A) Strategy alignment is a discipline that synchronizes analytics strategy with business strategy with the goal to help maximize the value created by the enterprise. It is an ongoing process, not a one-time activity.

B-A strategy alignment refers to using Analytics tools and capabilities in an appropriate and timely way, in agreement with the business strategies and goals. The goal is to make analytics more relevant and connected to business.

The business and analytics performance are tightly coupled, as an organization cannot maximize the return on investment in analytics if its business and analytics strategies are not aligned.

Why Strategy alignment?

Implementing an effective B-A strategy alignment model will help improve the organization’s performance leading to efficient processes, accessible business-relevant insights, maximizing value, and improving adoption of analytics in the organization. With this alignment, the Data Analytics Organization will be able to:

• Prioritize analytics efforts and initiatives as per business needs
• Understand business processes, vocabulary and definitions
• Design solutions that meet business needs and deliver value
• Partner to expedite “time-to-market”
• Control and manage risk and compliance issues
• Increase the agility of the business to react to changes
• Create greater integrations and collaboration between departments
• Maximize output from available resources
• Drive economical acquisition of tools
• Helps the organization gain or maintain the competitive advantage
• Foster relationship and collaboration with business

People

The people you work with ultimately determine the success or failure of the initiative and organization. Hiring and retaining talent is important but it’s just the tip of the iceberg. It’s equally important to foster the right type of mindset and organization structure to promote collaboration, consistency, agility and speed.

Skills

Proficiency in skills necessary to undertake the task at hand is extremely critical for quality deliverable. Attracting, hiring and retaining people with the required skill set has never been more important than today, in this fast-changing technological landscape and in the race to become a data-driven organization.

While it's, of course, important to develop the industry-specific hard skills, what's just as critical to the success are the soft skills. Soft skills are the tools we use to communicate and interact effectively with others. When industry-specific hard skills are coupled with soft skills, such as communication skills (written, verbal and listening), emotional intelligence, empathy, conflict resolution, negotiation and problem-solving skills, it allows to keep pace with the future of work and succeed.

Mindset

The Fourth Industrial Revolution’s impact on skills and jobs, with the advancement in technology and increasing automation, is rapidly making existing skills redundant, changing the way we work, and leading to a potential crisis at workplaces. We need to find or groom people who have the mindset to continually up-skill and re-skill to insulate from technology-driven changes to future-proof the organization and drive ahead of the competition curve.

Organization Culture

Having the right mix of skills will only do so much, if the organization culture is not conducive to support the ecosystem that is needed pursue strategic goals of the organization. An organization’s culture is its basic personality representing the collective values, beliefs, principles of employees, how people in the organization interact and get work done.

Since each organization is different and so are the people it in, there is no one size fits all. Making a cultural shift effectively requires time, motivation and compelling reasons, it won’t happen overnight. Deliberate time and thought should go into defining and setting the stage for the culture change that promotes collaboration, encourages and adopts new ideas and rewards the deserving appropriately.

A healthy and robust organization culture provides countless benefits, some of them are:

• Progressive culture
• Acceptance to change
• Consistent and efficient employee performance
• High employee morale
• Strong alignment towards organization's vision

Process

A process is, simply put, a set of interlinked steps designed to accomplish a specific organizational goal. Processes must be defined with a meticulous approach, research and time. The processes can be defined for daily operations, common company activities, or routine periodic tasks. After all, with standard operating procedures in place, training and on-boarding are faster and interruptions are reduced.

Although there is plethora of rationales, the main purpose for defining and documenting processes boils down to the following principal reasons:

• Compliance
• Operations Management
• Continuous Improvement and Adherence
• Other incentives include stability, consistency, regularity, uniformity, scalability, training and simplicity in execution

Compliance

The Data security and protection regulations have fundamentally transformed how businesses handle data. Any company that does not follow these norms face severe fines, depending on the severity and circumstances of the violation. In other words, the compliance is not optional.

For compliance to various regulations (GDPR, SOX…) the organization needs to define, communicate and enforce the data security and protection policies. These policies are defined to protect and secure all data stored, processed and shared in an organization. The policies including but not limited to lawfulness, purpose limitation, data minimization, data access, data accuracy, storage limitation, data security, integrity and confidentiality need to be defined and observed.

Operations Management

Every project/initiative that’s undertaken is supposed to deliver some value, either adding new capability or upgrading or addressing major gaps in the existing capability. This value is not an end but a journey.

As part of defining the processes, we need to consider the post go-live services to the business. These services ensures that operations are efficient in terms of utilization of resources, the solutions delivered are working as expected and issues are resolved within the defined service level agreements (SLA), so that the project/initiative continues to deliver value to the business.

Continuous Improvement and Adherence

Procedures themselves may not demonstrate compliance. Well-defined and documented processes along with records of measurable key performance indicators (KPIs) that demonstrate process results, capabilities and adherence will provide effective control mechanism and compliance to processes.

Reviewing KPIs for process effectiveness is one form of internal control, it should be an integral part of any business process. This review answers questions such as are the objectives accomplished, are the SLAs met, are the objectives meeting customer requirements?

The review process creates a foundation for improvement and mechanism to track adherence.

Technology

Understanding and defining the right technology mix for the Data Analytics Organization is a very critical task, well it’s not really a task, it’s a process. As it’s not something that can be done in a day, or in a year or even in 5 years. In fact, it’s not something that will ever be done at all considering how rapidly technology is evolving. The most important part of the process is to create a justifiable and logical digital transformation (DT) strategy that will guide through the process.

Adopting technology for the sake of adopting technology because “everyone else is using it” or “for the sake of the trend” is not DT. DT should be driven by the Data Analytics Organization’s strategy and not planned as a random assortment of initiatives. If DT is not incorporated in Data Analytics Organization’s strategy, then the technology is going to be limited in its value to the organization.

DT is not just about technology, but also about changing business processes, up-skilling or re-skilling people, engaging stakeholders and transforming corporate culture are factors just as essential to the success of the DT initiatives.

Data Analytics Life cycle

The following are the phases of the data analytics projects/initiatives life cycle:

• Business Requirement
• Data Preparation
• Model
• Analyze
• Maintain
• Evaluate

Now depending upon the delivery methodology (Waterfall, Agile, Hybrid…) used by the organization, these phases can be performed or implemented differently. Each of these phases must be implemented in some shape or form for delivering, maintaining and enhancing the high-quality solutions that translates to building an effective data analytics organization serving business strategic goals.

The first five phases are well understood, discussed and applied in the data analytics organizations. I would like to emphasize on the Evaluate Phase, as it is not as widely practiced or implemented.

As every organization has limited budget every year, there is a constant struggle to get the business sponsorship and resources for more projects. A key weapon to secure the sponsorship of the business is to highlight the insights and value the previous initiatives have brought to the business. This makes the Evaluate phase even more crucial.

In the Evaluate phase, the following activities can be planned:

• Track: Depending on the type of analytics, the impact of the model/solution to the business should be tracked over the long term because what performed well initially may not provide the same benefits or value as requirements, data, prioritization or business environment change over time. As tracking the solution results may also help to identify data quality problems, or other areas of improvement.
• Re-calibrate: The findings from tracking are then analyzed. Re-calibration of the solution(s) can be planned to ensure that the solution continues to add value or needs to be re-validated against the original requirement to see if the solution is still valid or even required with the current business environment and priorities.
• Optimize: As the data volume and number of users continues to grow, we need to constantly review the performance of the Data Analytics solutions and platform by either optimizing the solution, automating the processes or augmenting the resources, whichever is applicable.
• Train: No initiative or project is complete without training the end users. The training should document and explain the users on how to use the analytics solution, what results are expected and how to interpret the results. This is a continuous process as we re-calibrate or make enhancements to the solutions, the business users should be trained on the new features or capabilities and any impact on the expected results and their interpretation.
• Propagate: Although analytics adoption is rising, for organization to see substantial benefits from analytics solutions and platform, the business adoption is the key. Business adoption is a continuous process. Gaining business support and adoption can be planned by aligning with business goals, addressing compelling business issues, fostering relationships with the stakeholders, making data easily accessible, training, improving metadata and keeping pace with the change in business requirements, priorities and competition.

Conclusion

When implemented successfully and propagated effectively, data analytics organization can be transformative for the business, providing new valuable insights to assist in faster, more informed business decision making, backed by data.

Understanding and application of these building blocks, alignment of analytics strategy with business', motivated and skilled people, effective processes, right mix of technology and effectively implementing the analytics life cycle, is critical to build an effective and efficient Data Analytics organization that aids an organization in achieving its strategic goals.