As the amount of data organizations generate and capture increases exponentially, however, that volume becomes a blessing and a curse.
On one hand, more data potentially offers more value. Organizations can uncover critical insights about customers, track the health of complex systems, or view sales trends. But getting the value out of the data is not easy.
Five constraints that limit insights from business analytics data
Teams derive business metrics from many sources. Sources can include real-time application streams, user session data, and external business tools, such as supply-chain management, customer relationship management, and call-center solutions. These events represent activities that can affect the bottom line, such as sales order volumes, inventory counts, or delivery notifications.
Digital businesses rely on real-time business analytics data to make agile decisions. But for organizations that use traditional monitoring approaches, the volume, variety, and velocity of data in modern IT environments make gleaning real-time answers nearly impossible. To enable the collaboration between business and IT operations required for real-time business insights, IT context is critical. The main issues organizations face in gaining real-time insight from business analytics data using traditional methods include the following:
1. Data silos
More data residing in more places creates silos that limit organizations from accessing and maximizing data value.
Traditionally, IT stakeholders use observability data, and business analysts use business analytics data from separate sources. This separation limits what various teams can measure and derive from their individual areas, which hinders insights that drive decisions. Generating insights from these disparate sources can be time-consuming. Such insights demand coordination across multiple teams and require access to and capabilities of many different tools.
2. Fragile integrations
Much of the data business analysts rely on is stored in proprietary systems, where it’s difficult to access and often stale. To make the data more accessible, many organizations invest in complex and fragile integrations for business tools. These integrations often require resource-intensive coding, which makes them error-prone and difficult to maintain.
3. Lack of real-time context
To gain quick business insights without building complex integrations, many organizations turn to business intelligence tools, such as SAP Business Objects and Datapine. Although these tools can provide reporting, analysis, and interactive data visualization, they analyze data asynchronously, not in real time. These tools also lack IT context—an awareness of the systems and processes responsible for the business data. This lack of context leaves out vital details teams need to understand relevance and business impact.
4. High storage costs
The exploding scale of IT data in modern cloud environments can quickly become costly to store. Moreover, as organizations track additional key performance indicators, data volume only grows, which slows down searches. To make searches more manageable, teams rely on database indexing. Indexing can improve performance, but it can also be cumbersome to maintain and limit flexibility for future exploratory analytics. Teams also use data sampling from a small subset of data to identify patterns and short-term trends while minimizing costs. Although data sampling can reduce data volumes, it can also lead to sampling errors and inaccurate long-term results.
5. Search constraints
To analyze long-term trends using traditional methods, teams must rehydrate data that resides in “cold data storage” (older data that is no longer accessible for daily operations). Reinstating large volumes of historical data, however, increases consumption and bogs down queries. As the volume grows, these factors become increasingly challenging and costly.
To mitigate these challenges, teams often budget queries by identifying the questions to ask upfront. This approach requires building indexes to support these questions before ingesting data. Although this budgeting alleviates some overhead, it’s an inflexible approach that’s limiting and no longer sustainable.
How to bring observability data together with business analytics data
Businesses generate millions of events every day, from customer transactions to sales quotes to delivery exceptions and inventory changes. Yet, with the data housed in different systems and tools, it’s difficult to connect them with one another. That also makes decision-making based on contextualized insight difficult. If, instead, teams could easily unify these data streams and search on demand from a central place, the potential for gaining business insights increases. Business and IT teams can gain instant access to business, observability, and security answers in context.
With a modern approach to observability, teams gain easy access to real-time business analytics data without having to choose between low-cost storage and comprehensive insights.
An observability platform approach that uses a data lakehouse combines the benefits of a data warehouse with those of a data lake. This combination offers rich data management and analytics on top of low-cost cloud storage. A data lakehouse eliminates the need to choose the amount, locations, and type of data to ingest, retain, and analyze. As a result, teams no longer need to make those decisions upfront. A data lakehouse that’s purpose-built for business, observability, and security data provides the most effective way to store, contextualize, and query data from disparate channels. This efficiency gives teams immediate, actionable insights and drives automation.
Gaining insight from business analytics data
A data lakehouse unlocks the value of business data by centralizing it for contextual analysis. As a result, business and IT teams can enhance collaboration and improve agility by using real-time insights and operational analytics to make informed business decisions.
Business analytics data use case 1: Integrating third-party data
One use case is extracting precise, real-time business data from third party services to implement or improve a policy.
For example, over the past few years, a restaurant chain that operates in multiple locations has experienced an increase in mobile orders. To enable servers to pool tips fairly, systems must be able to allocate credit card tips to individual restaurant properties. Their payment service provider couldn’t provide that level of detail in a timely manner. By extracting the business analytics data from application flows and centralizing it in a data lakehouse, their teams can get the key financial data they need in real time. They also have this information in context with observability data to identify trends connected to their payment and location services.
Business analytics data use case 2: Optimizing the customer value chain
Another use case is optimizing business processes to reduce call center costs and improve customer service. Call centers are an important part of a complete customer experience, one of many available communication channels. How can a business gain the context necessary to understand where to optimize the customer experience for maximum value?
By combining observability data and business analytics data in a data lakehouse, it’s possible to connect complex business processes together. An example is an online order and a fulfillment process that includes contact center support. With visibility and automatic topology context for each step in the process, organizations can identify where to make improvements. This awareness decreases call-center demand and better optimizes their resources throughout the customer value chain.
How Dynatrace Grail brings context to business analytics data
To bring these kinds of real-life applications of observability and business analytics data to life, Dynatrace recently launched Grail. Grail is the only causational data lakehouse with massively parallel processing to unify and contextually analyze large volumes of IT, business, and security data from anywhere. Because it is indexless, answers to even unanticipated questions are instantaneous, with no data rehydration. Because it unifies data, Grail requires no fragile third-party integrations.
In conjunction with the easy, code-free access to business data with lossless precision from Dynatrace OneAgent, Grail enables you to ingest, retain, and query precise business data at a massive scale. This unified, contextualized access unlocks immense value from disparate data that may have been hard to achieve before. With IT context automatically applied to business analytics data, teams can accurately assess the impact of their technology on business outcomes and customer satisfaction.
With business analytics data easily ingested into Grail, Dynatrace enables business and IT teams to gain real-time business observability for instant answers to their most challenging business questions. Business teams can have the same real-time insights their IT counterparts enjoy while being confident in the integrity of business data.
Business teams can also understand a long-lasting customer journey spanning multiple touchpoints. Likewise, they can optimize a complex business process, capture critical business data from back-end or third-party systems, or something else. With this direct access, business teams can reduce the cost and delays inherent in using business intelligence solutions.
Dynatrace Business Analytics, powered by Grail and the Dynatrace core platform technologies, aligns the goals of business and IT teams through shared context, speed, and precision to deliver the answers you need to drive better business outcomes.
