Cloud Hosting

The enterprise journey to the single source of truth (SSOT) has been long and windy. The benefits of this direction are many, from a common data set to enterprise governance to unlocking valuable use cases and transformation across the enterprise, says Paula Hansen of Alteryx.

We can all relate to the desire for any employee to arrive at the same answer, once and for all putting to rest who has the better, more accurate data. But while organizations are on this journey, should they wait to pursue analytic insights and automation?

The exponential growth of data within the enterprise and the accelerating pace of business both suggest that pausing analytic automation until the single source of truth is completed will separate analytic leaders from laggards. Data is often scattered across various databases and applications, and different departments will migrate to a single source in a phased approach. Many business use cases, competitive decisions and transformation opportunities can be lost in this wait.

Simply put, driving business value through data and becoming analytically mature can advance in parallel with data centralization.

The reality is most enterprises today are pulling data from 6 input sources, from legacy databases and applications to modern cloud data warehouses and cloud platforms. A CFO pursuing a reduction in the time to close the quarter or a Head of Supply Chain wanting to optimize complex logistics is certainly pulling data from multiple sources and in multiple formats. In fact, the triangulation of different data sets and scenarios increases the quality of insights, creates better models and promotes enterprise collaboration.

For example, the development of a global go-to-market strategy likely pulls data from multiple CRMs, ERPs, legacy spreadsheets, third-party data sets and SaaS applications across a global enterprise. Different stakeholders within the business also want different insights from the data, ranging from partner insights to product insights to geographic insights to margin insights. The list goes on and on.

To reach the highest levels of analytic maturity what the International Institute of AnalyticsOpens a new window refers to as analytical nirvana and accelerate time-to-insight, it is important to democratize data and analytics to all of its employees. Organizations across every industry are increasing data literacy through analytics, using governance to scale responsibly, and evaluating the latest technologies like cloud, machine learning and generative AI to drive business outcomes faster than ever before.

See More: Data Warehouses: Why a Single Source of Truth Is Necessary for Customer Analysis

Centralized data science teams, while very valuable, lack the specific business expertise needed to effectively solve business challenges in each department. Data scientists are not trained as accountants, HR professionals, marketing experts or supply chain managers. Business context is required to ask relevant questions about your data and to reduce the time from insight to action.

The first step to increasing analytic maturity is to increase organizational data literacy. In this scenario, all employees are empowered to marry their domain expertise with the ability to ask more precise questions of the data, accurately analyze the data, and draw out valuable insights all through self-service technology that meets them where they are regardless of analytic skillset or analytic language preference.

Once you start your analytic program, there are many ways to democratize analytics and encourage greater data literacy. One approach is through gamification to increase employee engagement and upskilling. Jones Lang LaSalle (JLL), for example, established a gamification program that incorporates training to learn the functions of their analytics platform, provides users with challenges to work on their problem-solving skills, and issues certifications for awards and recognition of capability.

See More: Rise of Digital Banking Poses New Security Risks to Mobile Apps

Often, disaggregated analytics tools lack interoperability and create silos that result in complexity, duplication, and inefficiency for users and IT. Hybrid architectures that span on-premise and cloud-based data and applications are becoming the norm. Therefore, companies must implement the right tools and processes to pull disparate data types and place them into analytics processes wherever their data resides.

Further, the cloud can play a pivotal role in accelerating democratization through its flexibility, scalability, speed, and self-service. Using cloud-based analytic platforms streamlines IT management, removes overhead costs, and makes it easier for users to collaborate on their analytics solutions.

In todays fast-paced world, the ability to communicate insights quickly and effectively to stakeholders is critical. Generative AI will supercharge time-to-insight through its core value proposition and ability to rapidly accelerate content creation. Additionally, generative AI will accelerate analytic best practices and ML models across the enterprise.

For example, generative AI can automate communications that synthesize and deliver trusted analytical insights to stakeholders. The tone and language of the communications can be selected based on the intended audience. Instead of spending hours drafting reports or presentations for stakeholders, you can instead focus on absorbing insights and planning the best course of action based on results. This benefits users in several ways, including improved time-to-value, operational efficiency, and decision-making.

Governance is also a key consideration when scaling analytics across the enterprise. Governance doesnt mean democratization will come to a standstill. Proper governance helps organizations strike the balance of democratization at the speed the business demands with the controls that IT requires.

Establishing data governance means creating frameworks that define who can take what actions, with what data, in what situations, and what methods they can use. These principles guide data analytics at every stage of the collection of data and will be unique to each organization depending on the type of data, data systems, and regulatory requirements.

Data governance also helps to ensure that data is usable and accessible for analytics. This becomes even more important as companies adopt generative AI, where large language models rely on the quality of data inputs.

Digital transformation agendas have put data analytics at the center of every organizations focus in every industry. Whether your data is in a single location or not, empowering employees across the enterprise to make data-driven decisions through the use of analytics will directly impact company performance. Plus, employees stay longer in organizations that invest in their development and help them to automate the mundane and focus on value-add. Improving organizational analytic maturity can be done while maintaining proper governance. Analytic leaders will leverage their insights to uncover new revenue streams, improve operational efficiency, and stay hyper-competitive in todays evolving world.

What innovative data analytics strategies are you using to drive operational efficiency and decision-making? Share with us on FacebookOpens a new window , XOpens a new window , and LinkedInOpens a new window . Wed love to hear from you!

Image Source: Shutterstock

Read more from the original source:

Better Data Decisions on the Journey to a Single Source of Truth - Spiceworks News and Insights