In today’s competitive business environment, enterprises must effectively harness and maximize the power of data and artificial intelligence (AI) to be successful. To do so, companies need a powerful data stack that takes data from a state of chaos and turns it into a strategic advantage.
The opportunity to capitalize on enterprise data is a priority for data leaders. Our research finds that data and AI efforts are most often applied to customer-facing
Despite making large investments in data technology to support AI and analytics efforts, even advanced enterprises are plagued by challenges. A variety of issues lead to data complexity. Enterprises must deal with an increasing volume and diversity of data, including varying types and formats. For example, more than one-half of sales organizations (55%) report utilizing more than 10 data sources, including one-third (33%) that use more than 20. This increase in sources often leaves data fragmented and siloed throughout the enterprise. Organizations also struggle with data privacy and security, citing it as one of the top three challenges they face in adopting AI. Unstructured data sources, which have become even more important with the advent of generative AI, are problematic for many enterprises, with only 14% reporting that their unstructured data management technologies are completely adequate for their needs. And, increasingly, enterprises must be processing data in real time to meet customer expectations in sales and marketing processes.
Preparing data for analysis is a time-consuming and costly process. More than one-half (57%) of enterprises spend a majority of their time reviewing data for quality and consistency issues. Data comes from multiple systems in different formats with redundancies, gaps and other inconsistencies. Technology can streamline the data preparation process, but 60% of enterprises cite the cost of software as the most common barrier to improving sales and marketing analytics and data processes. Data practitioners are forced to take on the role of shadow IT, creating patchwork fixes and stopgap data pipelines to work around data processing obstacles. This approach is not sustainable, stable or reliable. Nor is it secure, which is a problem that is magnified by an increasing focus on data governance with new regulations and compliance requirements.
These challenges directly impact the efficacy of AI and analytics programs, and ultimately, bottom-line outcomes. Data complexity and data inconsistencies lead to a lack of trust and slow, poor decision-making. The lack of trust is exacerbated by poorly trained and poorly implemented generative AI models. It is important that these models and the prompts sent to them are supported with well-curated and trusted data to improve their reliability. These data challenges in turn result in inefficient, ineffective use of precious resources. Data teams should be able to use their data practitioners for what they have been hired to do: performing strategic data analysis and generating insights, leveraging data as value creators for the business. Instead, these data teams are relegated to tactical work and data workarounds just to get basic information on the table. This approach is not acceptable in a world where every team is expected to do more with less and where every resource has to prove its value.
Customers suffer as well. Fragmented, siloed customer experience data and generative AI hallucinations result in poor customer experiences (CX), which in turn lead to reduced customer retention, lifetime value and revenues.
Data leaders are at the forefront of unlocking the business value of data and using that data to create a competitive advantage. They are tasked with delivering high-confidence, low-risk business-ready data to users so teams throughout the enterprise can make smarter and faster decisions. The starting point is data integration, which brings data together from various sources into a central location for analysis. Despite promises of simplification from cloud-based offerings, data integration—especially customer data integration—is still complex and painful. The process of integrating and preparing data consumes more time than the analysis it supports. Despite all the improvements to technology stacks, the two remain disjointed and hard to use, evolving as separate systems from separate vendors, even if the vendors later combine through acquisitions.
While the move to cloud has helped enterprises integrate disparate data, utilizing the right tools is still key. AI can greatly simplify the entire data management
A comprehensive data management platform includes data matching, data quality and data governance with integrated data catalog capabilities. It also includes options for both extract, transform and load (ETL) as well as extract, load and transform (ELT) to take advantage of underlying data platforms. All of these tasks can be enhanced with AI to more efficiently unlock the value of customer data. In the data discovery process, AI automates the tasks of finding data and combining it into complete and accurate customer profiles. It enhances data integration by simplifying the process of joining data sources and loading data into analytics platforms. Using AI to identify anomalies in customer and campaign data improves accuracy and completeness of data cleansing and enrichment. In data governance processes, AI can detect personally identifiable information (PII) that requires special treatment.
AI can play an integral role in master data management by helping to deduplicate related records, detect address changes, identify and correct potential anomalies, and automatically tag content. It can also enhance data consistency across applications and analytics to create the most accurate and up-to-date customer golden record. Finding the right data for analysis and decision-making is made easier with AI-enhanced searches in a data marketplace. Pre-built connectors, application programming interfaces and process automation capabilities streamline the creation of repeatable data pipelines that connect applications across customer touchpoints.
Data is key to improving business processes, and thus business outcomes. Our research shows that enterprises use data to improve efficiencies, increase productivity and improve customer experiences. Customer data that is easy to find, understand and use makes it easier to unlock that data’s strategic business value, resulting in better CX and improved efficiency. Accessing data should be as simple and straightforward as possible. Streamlined customer data processes can be used to provide seamless, personalized engagement across multiple channels and systems, while ensuring that every interaction is based on accurate, current and complete information. Such a process allows revenue-generating service and support teams to make smarter, faster decisions.
Consider the case of an international athletic and footwear retailer that utilizes ML-based data-matching for a single view of the customer, which enables more accurate product recommendations and a better personalized experience. Having a more thorough understanding of customers and how their interests align with the company’s products results in increased sales and improved ROI.
Enterprises make large investments in their efforts to connect with customers, and these investments need to be directed toward the most efficient and effective avenues possible. More than 9 in 10 (94%) enterprises where marketing and sales are actively using data and analytics reported that it improved activities and processes. Better data management can provide higher quality customer and prospect information, allowing customer-facing teams to focus their efforts on campaigns and activities that deliver the most relevant offers going to the right people via the most cost-effective channels.
Consider the experience of a top wealth-management firm with a discerning clientele. Using data to create a more comprehensive and accurate picture of its customers gave its financial advisors the ability to make the right offer at the right time, thus improving customer service levels and increasing sales productivity.
Data teams work diligently every day to unlock the value of data for strategic business planning and results. Full visibility into customer data helps keep a finger on the pulse by enabling real-time understanding and responsiveness to changing market conditions and competitive threats. This visibility also facilitates the reconciliation of top-down strategies and bottom-up forecasts. Additionally, a single source of truth with accurate and consistent data from across the entire enterprise enables faster, better planning and forecasting. This same data improves communication by aligning the enterprise around a common strategy as expressed in its forecasts and targets, then enables better execution and measurement of the enterprise’s performance and effectiveness.
For example, an Australian retailer adopted a strategy to expand from a business-to-consumer focus to also include business-to-business sales. Automated quality assurance processes enabled the company to bring the majority of its product line—consisting of tens of thousands of stock-keeping units—online. The breadth, depth and accuracy of the retailer’s product information data supported B2B buyers’ purchasing processes and dramatically increased B2B sales, supporting the strategic shift.
Data and AI are critical to every facet of an enterprise, but complex data challenges hinder their potential impact. With streamlined data integration that addresses these hurdles,
data teams can increase effectiveness, efficiency and productivity, and lead the organization toward bottom-line improvement by taking advantage of all that AI has to offer. These benefits are especially important during challenging economic times when enterprises are expected to “do more with less” but have limited access to already-stretched IT resources. If the pipelines are robust and data is flowing in a stable way, business users can be equipped with self-serve capabilities that allow them to delve even deeper into the data.
But it is not just about whether the pipelines can handle the workload today. It is also important that as the enterprise scales, as it migrates to or integrates with new systems, the data management platform can support these changes and provide enterprise-grade stability at a cost that is affordable and predictable.
Enterprises building a data management stack should consider streamlined, intuitive technology that enables groups across the organization to utilize data to its full potential, while managing total cost of ownership. Ventana Research recommends identifying vendors that can deliver a broad range of robust enterprise-grade capabilities such as stable performance at any scale. These tools should provide simple, self-serve capabilities to democratize and speed up data processes making it easier to realize value from data. In addition, demand AI-assisted capabilities to speed up data processes, increase the accuracy of those processes and reduce dependence on IT. Consider only those technologies that provide secure and compliant access to data since it could include sensitive PII. These tools enable the enterprise to capitalize on their data to establish a competitive business and achieve revenue goals.