Data: The 'New' Oil Driving Business Success

In today’s digital age, the phrase “data is the new oil” has become increasingly relevant. You may well recall that phrase from getting on for 20 years ago, but the comment is arguably far more meaningful today than then. This analogy highlights the immense value of data in driving business success, much like oil has powered industries for over a century. However, harnessing the full potential of data requires a strategic approach, especially for business technologists who are at the forefront of this revolution. In this blog, I will explore how businesses can effectively use data, from traditional ERP systems to modern IoT devices and third-party data services, to fuel growth and innovation.

The Foundation: Core Business Data

For decades, businesses have relied on core data systems like Enterprise Resource Planning (ERP) to manage their operations. ERP data includes crucial information about financials, supply chains, human resources, and customer relationships. This data is the backbone of lots of  organizations, providing insights into the internal workings and helping leaders make informed decisions.

However, the value of ERP data extends way beyond its immediate applications. When integrated with other data sources, ERP data can reveal deeper insights and enable more strategic decision-making. For instance, combining sales data from ERP systems with market trend data can help businesses anticipate demand fluctuations and adjust their strategies accordingly, and whilst this may seem blindingly obvious to many of you, this is still on the ‘too hard to do’ list for many!

Expanding Horizons: IoT and Sensor Data

The Internet of Things (IoT) represents a significant expansion of the data landscape. IoT devices and sensors generate vast amounts of data in real-time (or near real-time), offering unprecedented visibility into various aspects of business operations. For example, in manufacturing, IoT sensors can monitor equipment health, predict maintenance needs, and prevent costly downtime. In retail, IoT devices can track customer movements and preferences within stores, enabling personalized marketing and improved customer experiences.

To effectively utilize IoT data, businesses need robust data management and analytics capabilities. This involves collecting, storing, and analyzing data from numerous devices and ensuring that it is integrated with other data sources. By doing so, businesses can gain a comprehensive view of their operations and uncover new opportunities for efficiency and innovation.

Leveraging Third-Party Data Services

In addition to internal data sources, third-party data services offer valuable external insights. These services provide access to a wide range of data, including market trends, consumer behavior, competitive analysis, and more. For instance, a business can use third-party data to understand how weather patterns affect consumer purchasing behavior or to benchmark their performance against industry standards.

While third-party data can enhance decision-making, it is crucial to ensure its accuracy and relevance. Businesses should carefully select data providers, verify the quality of the data, the true value it can bring, and integrate it seamlessly with their internal data sources. This integration enables a more holistic view of the business environment and supports more informed strategic planning. This is way more attainable than even a couple of years ago and make the job of ‘shadow-IT’ less needed than it used to be!

Ensuring Data Quality: Clean and Accurate Data

As businesses embrace the power of data, it is essential however to prioritize data quality. Clean, accurate data is the foundation of reliable insights and effective decision-making. Poor data quality can lead to incorrect conclusions, wasted resources, and missed opportunities.

To maintain data quality, businesses should implement robust data governance practices. This includes regular data audits, validation checks, and cleansing processes to remove errors and inconsistencies. Additionally, fostering a data-centric culture within the organization ensures that all employees recognize the importance of data accuracy and contribute to maintaining high standards.

Engaging Data Owners: Accountability and Passion for Data Quality

One of the critical factors in ensuring data quality is engaging the owners of the data. These individuals or teams are responsible for the creation, management, and maintenance of data within the organization. It is essential to hold them accountable for the accuracy and cleanliness of the data they handle. Accountability can be reinforced through clear expectations, regular audits, and performance metrics tied to data quality.

Moreover, changing the behavior of data owners to drive a passion for data quality is vital. This can be achieved through education and training programs that highlight the importance of data accuracy and its impact on business outcomes. Encouraging a culture of ownership, where data owners feel responsible and motivated to maintain high data standards, will significantly improve data quality across the organization.

Walking Before You Run: Caution with Generative AI

Generative AI represents one of the most exciting frontiers in data utilization. From creating content to designing products, generative AI offers numerous possibilities for innovation. However, businesses should approach this technology with a degree of caution and a clear strategy.

Before diving into generative AI, it is important to establish a solid foundation of data management and analytics. Ensure that your data is clean, accurate, and well-integrated across various sources. Additionally, businesses should experiment with generative AI in controlled environments, starting with pilot projects to understand its capabilities and limitations.

Moreover, businesses should be mindful of ethical considerations and potential biases in AI-generated outcomes. Implementing robust oversight and governance mechanisms can help mitigate risks and ensure that generative AI is used responsibly and effectively.

Driving Action: Practical Steps for Business Technologists

To harness the full potential of data and drive business success, business technologists should take the following practical steps:

1. Integrate Core and New Data Sources: Combine ERP data with IoT, sensor data, and third-party data to gain comprehensive insights.
2. Invest in Data Management: Implement robust data management and analytics platforms to handle diverse data sources and ensure data quality.
3. Prioritize Data Governance: Establish data governance practices to maintain clean and accurate data, and foster a data-centric culture within the organization.
4. Start Small with Generative AI: Experiment with generative AI in controlled environments, starting with pilot projects to understand its potential and limitations.
5. Focus on Ethical AI: Implement oversight and governance mechanisms to ensure responsible and ethical use of generative AI.

By following these steps, business technologists can unlock the full potential of data, driving innovation, efficiency, and competitive advantage. Data truly is the new oil. (Even 18 years after the initial use of this term), and with the right approach, it can power your business to new heights.

Ready to lead your team into the future with SAP’s proven solutions? Connect with me, share your experiences, and let’s drive change together. Comment below or message me directly. Let’s make your business operations smoother, faster, and more innovative.

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