Using business analytics can help companies extract valuable insights, make informed decisions, and drive growth. While many challenges can impede harnessing the power of data, most businesses that lack proper procedures, implementations, or standards will face four specific analytics challenges. Addressing these challenges and incorporating solution-oriented practices can help businesses unlock the full potential of data-driven decision-making.
Navigating Business Analytics Challenges
Business analytics tools offer immense promise, as they enable organizations to leverage data and extract meaningful insights to drive informed decision-making and gain a competitive edge. By using sophisticated techniques and algorithms, business analytics tools allow brands to identify patterns, trends, and correlations from vast amounts of data, uncovering hidden opportunities and potential risks.
Large organizations have a lot of moving procedures and policies that they must follow to maintain business operations. Similarly, business analytics are comprised of many moving parts. Different types of business analytics can provide different insights for organizations to make data-driven decisions, optimize operations, improve customer experiences, and drive business growth.
Types of Business Analytics |
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|---|---|---|
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Type |
Description |
Technique |
|
Descriptive Analytics |
Uses description to understand what has happened in the past. |
Data aggregation, data visualization, reporting, data exploration. |
|
Diagnostic Analytics |
Aims to determine why specific events or patterns occurred. |
Root cause analysis, data segmentation, drill-down analysis. |
|
Predictive Analytics |
Uses historical data to predict future outcomes. |
Regression analysis, time series forecasting, data mining, modeling. |
|
Prescriptive Analytics |
Produces recommendations and optimal solutions for decision-making. |
Optimization models, simulation, decision trees, scenario analysis. |
To fully navigate the business analytics landscape and incorporate best practices for all types of business analytics, organizations must understand the business analytics challenges that can arise when implementing and optimizing internal practices. These challenges encompass both hardware and software issues, data management problems, and data governance hurdles. Because each challenge comes with its own issues and solutions, they all complement each other to create an overview of the complications that can make implementing and optimizing business analytics so difficult.
1. Maintaining Data Availability
Data availability is defined by the International Information System Security Certification Consortium (ISC)2 as “the amount of which systems and data are accessible at the times in which the required/authorized users need them.”
Data availability is critical because business operations depend upon it for decision-making, operational efficiency, customer experience, and risk management. Even in the most ‘perfect’ systems, experts cannot guarantee data availability 100% of the time. This makes maintaining data availability a constant, top-priority battle for IT professionals.
Solutions
To maintain data availability, IT professionals can take advantage of data cleaning and backup procedures to avoid single points of failure and speed recovery times. Multiple technical solutions, such as Snowflake’s data warehouse and Salesforce, make it easier for organizations to assure resiliency and data consistency with modern devOps tools and secure testing environments.
2. Ridding Operations of Data Silos
Data silos refer to isolated pockets of data within an organization that are not easily accessible or shareable with other departments or systems. This occurs when different teams or business units manage and store data independently, often resulting in data duplication, inconsistencies, and fragmentation.
Data silos can hinder collaboration, integration efforts, and impede the organization's ability to derive cross-functional insights. They often lead to inefficient processes, redundancy, increased costs, and limited visibility in the organization's overall business analytics landscape.
One of the most common, direct causes of data silos is inflexible employees and departments that refuse to adapt and use data processes and best practices. This is a big challenge for organizations because they cannot simply resolve things by implementing technology. Users must change their daily habits to combat data silos effectively and for data governance to prosper.
Solutions
Overcoming data silos typically involves implementing data integration strategies, breaking down communication barriers, and establishing centralized data repositories or warehouses for better data sharing and accessibility. Reaching out to a specialty tech consultant firm can help eradicate data silos, as consulting firms create unique strategies and solutions for organizations to incentivize data governance among employees. This often includes training courses, data visualization techniques, collaboration, and data sharing policies.
3. Sustaining Connected Data
Connected data is defined as discrete entities of data that are linked via network relationships. The growth of an organization relies heavily on connected data to unlock valuable insights and make informed decisions. By linking and integrating various data sources within an organization, like customer information, operational data, and market trends, organizations can gain a comprehensive view of their operations and the broader business landscape.
Interconnectedness allows for deeper analysis, better prediction, and enhanced understanding of complex relationships and patterns. Moreover, connected data promotes collaboration and data sharing across departments and teams, fostering innovation and driving efficiency.
It’s difficult to sustain connected data in large organizations with complex data because of the multiple departments, business units, and systems that operate alongside one another. To manage the data between departments, organizations need to ensure their architecture accommodates growth and that all employees are thoroughly trained and incentivized to collaborate. Whether the organization has implemented connected data procedures and policies from the beginning, as an organization grows, procedures and policies need consistent maintenance and updating as the volume and variety of data increases. Greater varieties and volumes of data makes maintaining data quality, integrity, and governance more difficult. The presence of legacy systems, varying data formats, and disparate databases further complicates the task of establishing and sustaining connected data.
Solutions
Interoperability is important for sustaining connected data. By ensuring compatibility and standardization, interoperability facilitates the smooth flow of data, allowing organizations to connect and aggregate information from various departments, systems, and external sources.
Connected data can be maintained by:
- Incorporating automated relationships on all new entities identified as pre-existing.
- Utilizing cloud services for data storage and connection tools.
- Implementing data-centric artificial intelligence.
One platform solution that encourages interoperability and connected data is MuleSoft. MuleSoft is platform that helps connect applications and data to maintain connected data with better performance capabilities.
Another platform that helps sustain connected data is a customer data platform. CDPs collect customer data from all your first-party data sources (like websites and apps) and create persistent, unified consumer profiles based on that data. Tealium is one top-performing CDP that helps organizations build out 360-degree views of their customers with connected business analytics.
4. Adapting Technology Infrastructure
The success and efficacy of an organization relies heavily on the health and functionality of their tech infrastructure. A tech infrastructure that lacks support for business analytics can hinder data-driven decision-making, limit market trends and customer behavior, and impede the organization's ability to stay competitive in a rapidly evolving business environment.
To transform your digital infrastructure, you’ll need to address a few challenges. This includes constraints on the budget and existing systems along with privacy challenges, user adaptation challenges, and more.
Solutions
Updating systems and investing in advanced analytics tools, like Adobe Analytics, can help adapt your technology infrastructure in support of business analytics. Hiring third party consultants with specialized expertise can help quicken the process of implementation and adoption. It’s important to consider future scalability when selecting technology vendors or partners.
Transforming Business Analytics Challenges With Concord
When navigating business analytics challenges, consultants can provide valuable expertise, experience, and bring a fresh perspective to the table. They have in-depth knowledge of industry best practices, data analytics techniques, and the latest tools and technologies. Specialty tech consultants can help organizations overcome challenges by providing guidance on maintaining data availability, ridding operations of data silos, sustaining connected data, and adapting technology infrastructure.
Getting Started
Concord is a consultancy that combines technology and industry depth with a get-it-done culture to enable resiliency, efficiency, and innovation. Whether you are looking to improve customer satisfaction, implement effective data strategies, optimize cloud applications, or anything in between, we can help.
Contact us today to learn more about how to build a customer experience strategy in retail, our Technology and Data Integration Services, and how we can help your business thrive.