Democratizing Data Analytics

Transforming Business with Data

In today’s data-driven world, understanding and leveraging insights from data has become essential for staying competitive and driving brand growth. However, for many businesses, accessing and analyzing data is considered an overwhelming and costly endeavor, a set of practices reserved for large enterprises with extensive resources. But with advances in tools and accessible data, no business should be without data analytics capabilities. A brand’s data is its competitive advantage, but only if activated.

Data Analytics for Every Brand

Data is no longer a luxury; it is a strategic asset that holds valuable insights waiting to be uncovered. With increased accessibility to data analytics, businesses of all sizes can harness the full potential of their data.

One of the greatest features of data analytics is scalability: a multitude of data science practices and tools exist to match the capabilities of any business.

Few brands have escaped the volume of publications from vendors, practitioners, and industry journals describing the multitude of benefits arising from data analytics. However, one of the greatest features of data analytics is scalability: a multitude of data science domains exist to match the capabilities of any business. Whether a brand is installing a new visualization tool, integrating their first full CRM, or training algorithms to develop brand specific ML-models, brands at all levels of data maturity are able to benefit from their own data. 

In fact, today’s entry barriers are minimal thanks to accessible relevant data, cheap storage capacity, and available processing power. Add to these cloud-based analytics tools, and brands minimize their infrastructure needs while gaining data analytics dividends.

When brands embrace and extend data capabilities, the result is transformative innovation and customer experiences that benefit everyone.

While the benefits of data analytics are rarely contained within hard boundaries, some of the largest areas of return include unleashing innovation, enhancing customer experiences, and, ultimately, transforming the business.



Unleashing Innovation 

By placing data analytics tools in the hands of more people, we open the doors to creativity and innovation. When individuals across an organization have the ability to explore and analyze data, they can uncover new opportunities, identify emerging trends, and drive innovation that leads to a competitive edge. Innovation can come from the simplest descriptive analytics, using readily available data, familiar analysis techniques, and clear objectives. As brands progress in data science maturity, innovation may be driven by more sophisticated predictive machine learning algorithms that uncover exponential return, which might be implemented with a series of simple actions that fulfill targeted parameters of a model.

  • Descriptive analytics offers insights into historical data patterns and trends, aiding businesses in identifying areas for improvement and refining strategies.
  • Diagnostic analytics helps businesses understand the underlying factors contributing to specific outcomes or issues through root cause analysis, enabling targeted improvements and innovative processes.
  • Predictive analytics allows businesses to forecast future trends, demand patterns, and market shifts by analyzing historical data and external factors, aiding in proactive innovation.
  • Prescriptive analytics goes beyond data analysis, providing recommendations and optimization strategies for data-driven decision-making, guiding innovation and achieving desired outcomes.
  • Implementing artificial intelligence (AI) and machine learning (ML) techniques empowers businesses to achieve transformative innovation by analyzing vast amounts of data, discovering patterns, and making real-time predictions or product decisions. This can revolutionize operations with automated processes, personalized experiences, dynamic pricing models, and predictive maintenance.

Enhancing Customer Experiences

Data democratization enables businesses to better understand their customers’ needs, preferences, and behaviors. Armed with these insights, companies can personalize experiences, tailor marketing efforts, and create more meaningful connections, fostering loyalty and long-term success. Customer experiences can be enhanced from ad platform capture of attributes and interests to align offerings with preferences, or simple web analytics capture of interests across a well-developed brand website. With more sophisticated multi-touch attribution solutions, businesses can analyze granular customer journeys to isolate and aggregate customer data that demonstrates preferences in real time. Applying ML and AI-powered analytics, brands can use the customer journey data to predict customer value (e.g., CLV), providing targeted offerings that match consumer needs and generate the greatest ROI.

  • Personalized recommendations use customer data to provide tailored product suggestions based on past purchases, browsing behavior, and demographic information, enhancing customer engagement and satisfaction.
  • Businesses can leverage data analytics to segment their customer base and create targeted marketing campaigns, customizing messages, offers, and content for specific customer segments to drive higher engagement and conversions.
  • Customer journey analysis involves analyzing customer interactions across various touchpoints, such as website visits, social media engagement, and customer service interactions. Insights gained from this analysis help identify pain points, streamline processes, and optimize touchpoints for a seamless and satisfying customer experience.
  • Creating an omnichannel customer experience entails integrating data from multiple channels and touchpoints, including online and offline purchases, mobile apps, and customer support. By leveraging data analytics, businesses can deliver consistent and personalized experiences across all channels, ensuring a seamless customer journey.
  • AI-powered chatbots and virtual assistants enable businesses to offer instant and personalized support to customers. By employing natural language processing and machine learning algorithms, these sophisticated systems understand customer queries, provide relevant information, and offer personalized recommendations, elevating the overall customer experience.

Empowering Business Transformation

When data analytics becomes well-integrated across an enterprise;rise, businesses can gain a deeper understanding of their operations, customers, and market dynamics. This knowledge empowers them to make data-driven decisions, optimize processes, and transform their brand from the inside out, for all team members and customers across the addressable market. Two the greatest data analytics results and multipliers for growth-focused brands are closed-loop marketing and data-driven decisioning.

  • Closed-loop marketing allows businesses to connect all data elements in the business life cycle and customer journey–all customer touches, marketing elements, conversions, and revenue. The result powers a holistic understanding of the customer, measures actual return of all marketing elements, and aligns all brand activity with revenue.
  • Data-driven decisions beyond descriptive analytics allows brands to make informed decisions from a wide variety of inputs, ensuring marketing mix and all associated parameters result in the greatest performance results.

Closed-loop marketing and data-driven decisioning together enable brands to tackle almost any challenge within the product or service life cycle. While businesses often focus on the marketing and sales domains of impact, a mature data science organization can optimize supply chains, detect fraud, and even maintain manufacturing quality. 

Cost-Effective and Scalable Solutions

Data analytics tools and platforms are now available to all levels of organizational maturity; in fact, many tools are a great entry point that can scale as an organization grows in data sophistication. 

Below are five analytics solution areas that are open to almost any brand, and fully scalable as brand needs and capability increases.

Data Visualization Tools

  • Beginning Stage: Brands just starting with data analytics can deploy user-friendly data visualization tools that offer pre-built templates and drag-and-drop functionality. These tools allow businesses to create basic visualizations and gain insights from their data without requiring extensive technical expertise.
  • Scaling Up: As businesses grow and data complexity increases, they can transition to more robust data visualization platforms that offer advanced customization options, interactive dashboards, and real-time data updates. These tools enable deeper analysis, collaboration, and sharing of insights across teams and departments.

Cloud-based Data Storage and Processing

  • Beginning Stage: Small brands can start by leveraging cloud-based data storage solutions, such as cloud databases or file storage systems or CRMs. These offer scalability, cost-effectiveness, and easy data accessibility.
  • Scaling Up: As businesses accumulate larger volumes of data and require more sophisticated data processing capabilities, they can adopt cloud-based data warehouses or data lakes. These platforms provide the scalability and processing power needed to handle big data analytics and support advanced analytics techniques.

Self-Service Analytics

  • Beginning Stage: Brands can begin with self-service analytics tools that have intuitive user interfaces and require minimal technical knowledge. These tools enable business users to explore data, create basic reports, and gain actionable insights without heavy reliance on data analysts or IT support.
  • Scaling Up: As businesses grow, they can implement more advanced self-service analytics platforms that offer a broader range of features, such as advanced data modeling, predictive analytics, and collaborative capabilities. These tools empower users across the organization to conduct complex analyses and make data-driven decisions independently.

Marketing Attribution

  • Beginning Stage: Businesses using digital ad platforms and Google Analytics can employ the built-in attribution modeling within the platforms. While limitations exist for these free attribution solutions, they provide a starting point for analyzing customer journeys, including top and bottom funnel marketing element efficacy. 
  • Scaling Up: Brands with greater data maturity should invest in full-path multi-touch revenue attribution. With these sophisticated tools, businesses can query customer journeys by dozens of dimensions, answering key questions across the customer life cycle. These advanced MTA tools can uncover revenue and efficiencies across the marketing mix.

Machine Learning and Artificial Intelligence (AI)

  • Beginning Stage: Brands can begin by exploring basic AI concepts and implementing pre-built models offered by data analytics platforms. This allows for automated data analysis and pattern recognition without the need for extensive data science expertise.
  • Scaling Up: Larger businesses can invest in building in-house data science teams or partnering with data science consultancies to develop custom machine learning models and AI algorithms. These models can be utilized for advanced use cases such as customer segmentation, fraud detection, or demand forecasting.

By adopting contemporary data analytics tools and practices that align with their current needs, businesses can gradually scale their data analytics capabilities as they grow and require more sophisticated analytics solutions.

Growing Data Analytics Capabilities

While the entry into data analytics has never been easier, businesses who plan to continuously evolve and expand product, services, and market share must develop their data science maturity. Brands should simultaneously exploit the accessible tools and practices above while assessing and developing their key data science domains:

  • Organization and People
  • Infrastructure
  • Data Management
  • Analytics
  • Governance


Organization and People

A brand’s organizational awareness and data culture dictate the degree of analytics adoption possible. A business may begin with just a few people focused on data projects, siloed within their own departments. While these individual early projects uncover data and impact performance, they are often considered to be novelties, not a core approach to business performance. A business reaches foundational data analytics once an awareness of the value of data becomes widespread.

To move beyond a foundational data analytics level, multiple staff levels are involved, and executive teams recognize that pursuing data solutions is key to achieving goals. The company recognizes a fundamental competitive advantage from data, and the business strategy is amenable to insights from data to drive practices across the enterprise.


Organizations may begin with little infrastructure, often internally developed, with patched legacy databases and spreadsheets. Often data volumes are incomplete, and most data activities are run on desktop hardware.

To develop data infrastructure requires a general consensus and recognition that an enterprise data solution is necessary. Pain points from manual, error-prone work, and inconsistent practices drive awareness. Implementing an enterprise data platform standardizes all units, automates data processes such as collecting, cleaning, and integrating. The result is a data workflow model that emphasizes collaboration, modeling, and tracking.

Data Management

Companies begin with local data management, department-level efforts often residing with the data owner, the business line, or the IT group that created the database and assets originally.

Data management becomes a powerful activity once a brand adopts an enterprise system. Enterprise data allows realistic consideration of initiatives to identify, organize, and evaluate all current data assets.  While the need to address large data volumes arises earlier, enterprise management allows brands to conceive of strategies for big data.


Early brand data analytics is often limited to financial and compliance data, areas of legal and fiduciary responsibility. Eventually 1-2 departments might use a specialized toolset, but access is unshared beyond siloes. Foundational level organizations can have successful data analytics projects in multiple business lines.

To move to more impactful, broader performance, analytics is integrated, business-centric, and projects span business lines and functions. Insights are applied to business activities and quantified. Teams go beyond descriptive and diagnostic, commonly using predictive and prescriptive analytics. Finally, use cases now include AI/ML throughout the organization. Not only are data-driven insights an established part of the culture, but integrating results into new practices creates measurable value for the company.


Data control often begins in IT or with legacy ownership within a department. Access is limited, and requests for data are slow due to IT and small department constraints. At this point of data maturity, data is not designed for everyone. As a result, individual departments enforce inconsistent policies and controls in siloes, with no centralized governance. Most importantly, practitioners do not know what data is available from other teams. Opportunity for error and risk is high.

In advanced levels of integration, accessible tools span the organization and help automate governance.  Access to data is managed quickly and efficiently. Confidence in data governance is reflected in the culture of data-driven deicisioning across business units.

Moving Forward

Once brands understand and begin to quantify the value of data analytics within their own organizations, the appetite for broader and deeper practices can grow faster than capacity. While success stories help drive advances, brands should constantly assess data analytics readiness to limit missteps and prepare for a successful evolution of data science maturity.

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