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The Real Deal for Transforming Data into Business Value

Heather Holst-Knudsen
October 12, 2023

Data analytics and AI have become integral tools for businesses to stay ahead in today's competitive marketplace. Are you tapping into your full data potential to drive decision-making processes?

In today's marketplace, data is more valuable than ever before. Businesses that use data analytics and AI to inform and support their decisions often outperform their competitors who rely on gut instincts alone. A recent HBR Pulse survey on transforming data into business value through analytics and AI found that 75% of respondents say having a data-driven culture is critical to their organization's overall success. But, where is your go-to-market source of truth? How can you ensure that the data you are using is accurate and up-to-date? In this post, we'll explore the survey results and discuss some effective strategies for transforming data into business value.

Unified Source of Truth in the Cloud

The survey found that organizations already highly data-driven before the pandemic doubled down and became even more data-driven while struggling organizations fell further behind. Leaders in the survey invested in and accelerated data, analytics, and AI initiatives at higher levels than their counterparts. The reason for this is obvious: with COVID-19 completely transforming how businesses operate in weeks, executives were more inclined to adopt data-driven cultures and accelerate industry-specific solutions, including AI.

In addition, it was discovered that having a unified data cloud is important for organizations that desire significant value from their approach. A Unified Cloud approach enables businesses to share data more efficiently and effectively, increasing collaboration and reducing redundancy, resulting in the ability to identify opportunities for innovation, market entry, competition, scale efficiency and agility, reduce risk, and improve operating margins.

The Challenge and Critical Need for Data Democratization

“While it appears that many companies have their data management acts together, in fact, many don’t,” says Doug Levin, executive in residence at Harvard Business School and lecturer at the Harvard Business Analytics Program.

However, the survey found that organizations still struggling to keep up often face challenges in analyzing data across multiple sources and data quality issues.  In the past, data silos have been creating barriers to analyzing data across the whole corporation and restricting access to real-time data. Sourcing data from various places remains challenging, including complexities in integrating/consolidating data from several systems/sources. Additionally, data quality issues often arise from poor data governance practices.

Multicloud adoption is emerging as a popular solution, but it presents challenges in data governance/management and service integration and management. However, democratizing access to data and analytics tools and AI capabilities is key to remaining competitive.

Bye Bye Data Chaos, Hello SOT in the Cloud

Organizations with data complexity due to high levels of daily transactions, mergers and acquisitions, diverse customer segments, and revenue streams have even greater challenges.

CRM and data warehouses are used for data management, but they are not well-suited for most B2B organizations. For instance, a CRM cannot handle detailed digital data such as product analytics, payment/booking/subscription information, call intelligence data, among other areas. Moreover, data warehouses limit you to high-level dashboards that provide minimal insights, or you have to hire a data science and engineering team that can translate the data into valuable insights. Organizations in highly transactional environments with multiple revenue sources and customer segments such as business information, media, events, and marketing platforms require more innovative approaches to data management and reporting.

Now organizations can use end-to-end data management and advanced analytic solutions such as Insightify. Insightify sits on top of core systems such as CRM, Marketing Automation, Finance, Operations, CDP, and even existing business intelligence tools and seamlessly aggregates data from decentralized sources, unifying it into a Single Source of Truth (SSOT) at the platform level. Insightify preserves data in its original format at its source so that businesses can avoid the high costs and risk levels associated with waterfall-level data transformation projects.

Using this unique approach, users are empowered with high-level dashboards and visualization tools coupled with in-depth drill-down capabilities to diagnose, interpret, and prescribe actions and decisions.

Measuring  Financial Returns

Finally, the survey highlights the importance of measuring and reporting data and analytics investments, business value, or outcomes. Investing in data and analytics is only useful if it can deliver concrete business outcomes, such as new product/service introduction, operational efficiency, customer satisfaction, revenue, and market share. Leaders' organizations reported improved performance in each of these key areas. Maximizing business value from data requires leadership support and enterprise-wide strategies for data and analytics.

In conclusion, organizations that prioritize their data analytics initiatives can expect significant returns. With data-driven cultures becoming must-have requirements for modern-day business, businesses must ensure they have a significant head start over their competitors. Leaders who invest in data, analytics, and AI initiatives position themselves to address the effects of disruptions while driving innovation effectively. The right approach, using the correct data from the right platform, can create significant business power. Organizations that utilize effective strategies for transforming data into business value can improve their operational efficiency, customer satisfaction, revenue, and market share while also 21differentiating themselves from their competitors.

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