As AI technology continues to evolve, it is expected to drive even greater value for organizations
What are some of the common problems data analysts encounter during analysis and what are the technical tools that you have used for analytics?
In solving any problem statement using data and analytics, data analysts spend about 70% of their time in data engineering activities. This involves accessing, retrieving, cleaning, and processing data before it is ready for modeling and insight generation. Some of the challenges they face during this phase are:
Availability of Data: Companies may not be capturing and storing the data required whether internal and or external data for analysis. This results in them relying on third party subscriptions.
Access to Data: Often, obtaining access to relevant source systems or databases can be a time-consuming process, often requiring data analysts to navigate multiple hurdles and obtain approvals making the process time-consuming.
Quality of Data: Data analysts must ensure that data quality attributes, such as consistency, accuracy, completeness, auditability, orderliness, uniqueness, and timeliness, are addressed before beginning any modeling exercises, as failure to do so will result in a "garbage in, garbage out" scenario.
Other challenges they face during the lifecycle of an analytics project include:
Access to Tools and Technology Stack: It is crucial for data analysts to have access to or subscriptions for high-quality data processing tools and the appropriate technology stack. Without the right tools, the output may be suboptimal (e.g., Google or Adobe Analytics subscriptions for web analytics). These tools should also be compatible with the organization's overall technological infrastructure to avoid inefficiencies.
Business Context: In certain instances, the data analysis being performed by analysts may be highly specialized and niche, such as conducting nuanced analyses for the R&D function of a life sciences company or regulatory analysis that requires a deep understanding of the industry and its regulations. In such scenarios, it is imperative that data analysts work in close collaboration with the appropriate business stakeholders or subject matter experts to ensure accurate and relevant analysis.
Analytics leaders should design a data and analytics strategy which is aligned to their enterprise strategy.
To overcome these challenges, data analysts employ a range of technical tools, including SQL, SAS, R, Python, Synapse Analytics, MS PowerBI, Tableau, Alteryx, and more. These tools enable data processing, advanced statistical analysis, and data visualization.
Give us an example of the creative ways that would help to build analytics capabilities that enable successful business outcomes.
To build, grow and scale analytics capabilities, analytics leaders need the following in place – a data and analytics strategy, people, operating model, and technology.
Data and Analytics Strategy: Analytics leaders should design a data and analytics strategy which is aligned to their enterprise strategy. An ROI mindset while prioritizing analytics use cases is critical when it comes to business alignment and adoption. Additionally, leaders should build a data-driven culture within the organization. This involves educating all stakeholders on the importance of data and analytics and encouraging them to use data in decision-making processes.
People: Hiring, engaging, and retaining data science professionals is not an easy task. Businesses should take a hybrid approach partnering with reputed analytics consulting firms for talent and outside-in perspective in parallel to their own efforts of building a strong in-house time. Over time, the ratio of external consultants to in-house analytics professionals should change. Many companies also partner with reputed universities and get fast and innovative solutions to challenging problem statements.
Operating model: Designing the organizational structure is a critical task for leaders, as it has a direct impact on operational efficiency, productivity, and the success of analytics initiatives. Additionally, the ability of this structure to foster strong collaboration with support functions and businesses is of utmost importance.
Technology: As the saying goes, “Give ordinary people the right tools, and they will design and build the most extraordinary things”, organizations should very carefully invest in technologies that align with their data and analytics strategy and helps data scientists generate actionable and high ROI insights. Lastly, it's important to continuously measure and track the impact of analytics initiatives on business outcomes. By regularly monitoring key performance indicators and adjusting strategies as needed, organizations can ensure that their analytics capabilities are driving the desired results.
"Organizations are leveraging AI technologies like machine learning, NLP, and computer vision for high value use cases across business functions like sales, marketing, supply chain and risk management."
With digital transformation, will companies have to reassess their existing business technology stack?
To begin, it's important to understand digital transformation (DX). The core components of DX are Digitization, Digitalization, and Data Monetization. Digitization is about ensuring the right data infrastructure and governance of enterprise data. It ensures organizations have the right foundation for analytics. Digitalization is converting workflows and processes into software and hence is about software development, automation and creating new digital-driven revenue streams. Data monetization refers to leveraging enterprise data, algorithms, and technology to generate value – increase revenue, improve market share, reduce costs and expenses, mitigate risks, etc.
Undoubtedly, digital transformation mandates a thorough reassessment of a company's existing business technology stack. The three components mentioned above necessitate a comprehensive evaluation of the current technology infrastructure, and potentially, the replacement of outdated legacy systems with more cutting-edge solutions that can effectively cater to the demands of a digitally focused enterprise.
Additionally, companies will need to consider the integration of new technologies into their existing infrastructure. This includes evaluating the compatibility of new technologies with existing systems and determining how data will flow between different platforms.
Ultimately, the goal of reassessing the business technology stack is to ensure that the organization is equipped with the right tools and technologies to support digital transformation initiatives and remain competitive in a rapidly evolving digital landscape.
How is AI driving digital transformation across organizations and creating significant value?
AI is driving digital transformation across organizations by enabling them to make better decisions, automate processes, and create new business models. AI-powered solutions can analyze large volumes of data, identify patterns and trends, and provide insights that can inform strategic decision-making. This helps organizations to stay ahead of the competition by identifying opportunities and risks and making data-driven decisions.
Organizations are leveraging AI technologies like machine learning, NLP, and computer vision for high value use cases across business functions like sales, marketing, supply chain and risk management. Some popular use cases include scoring sales leads, predicting customer churn, forecasting demand, predicting machine or equipment failure, predicting fraud, automating and faster document processing, etc.
These use cases help organizations increase revenue, reduce costs, improve customer satisfaction, and create sustainable competitive advantage. As AI technology continues to evolve, it is expected to drive even greater value for organizations in the years to come and play a critical role in shaping the future of business.
Tell us about a significant data analytics project you worked on and how the insights you gained from it benefited your client organization.
Not too long ago, I had the privilege of assisting a Fortune 1000 company in designing a data-driven Revenue Expansion Program. The endeavor involved consolidating customer and associated data from various source systems, which was followed by leveraging a range of statistical and machine learning algorithms to carry out the following analytics:
Customer segmentation process was instrumental in identifying the heterogeneity among different customer segments and their respective personas. This strategic insight allowed for more precise targeting and increased effectiveness in sales and marketing campaigns.
Lead scoring ensured the sales personnel were allocated fewer but higher quality leads.
Customer churn allowed the company to gain insights into the factors driving it, thereby enabling them to launch a timely retention campaign aimed at retaining their most loyal and profitable customers.
Predict customer lifetime value (CLV) so they could pay more attention to the high CLV segment.
Predict upsell and cross-sell opportunities which helped them grow revenue from their existing customer base.
All the above analytics modules were ultimately converged into a single framework which helped them improve market share, increase revenue, reduce churn, and improve CX and CSAT.