Achieving Data Resilience: 4 Steps to Unleash the Power of Data Analytics

Achieving Data Resilience: 4 Steps to Unleash the Power of Data Analytics
Posted : May 30th, 2024

An exorbitant amount of data surrounds the current business landscape and organizations are striving to collect more of it than ever before. The relevance and trajectory of data harnessing is rapidly rising as it is no longer just a trend; data is now regarded as the norm. However, according to recent research, only 19% of executives can assert that they have established a data culture in their company.
This proves that bridging the gap between collecting data and systematically leveraging it is the need of the hour, which is where the spectrum of data analytics comes in.

The Evolution of Data- Then and Now

The concept of data in its nascent stage was more of a backward-looking model that involved reporting, filtering, and analyzing the collected data points. To quote Robin Sutara, Field Chief Data Strategy Officer at Databricks who was recently featured as an acclaimed guest speaker in our ‘Enabling Digital’ podcast series, “Over the years, there has been a transformative shift in this outlook, as organizations are now striving for a proactive, forward-thinking data strategy that can help build a scalable, predictive, and preventative data-driven business.”



Transitioning from Traditional Analytics to AI-Powered Data Analytics

Traditional analytics has long been the cornerstone of business intelligence as it provides crucial insights into past metrics and performance but falls short in setting futuristic goals or predicting future trends. Artificial intelligence (AI), on the other hand, marks a significant advancement in data-driven decision-making. Unlike traditional analytics, AI can process enormous volumes of data in real time, enabling businesses to uncover emerging patterns and trends that traditional methods would miss. This predictive ability allows organizations to anticipate market shifts and validate consumer behavior.

Moreover, the emergence of data warehouses and cloud computing has also played a pivotal role in reshaping the data analytics infrastructure. Organizations that previously relied on in-house data centers with fixed capacities for storage are now moving towards data warehouses based on cloud computing technologies. Unlike traditional databases, data warehouses can provide on-demand access to vast computing power, enabling quick and complex data processing. So, how can organizations leverage the transformative journey of data to their advantage and gain a competitive edge in the market? Here is a pragmatic 4-step approach!

1. Collecting Data

The first step to adopting any tool or technology is knowing and understanding the prerequisites. In this case, cataloging your data assets and citing its sources is of paramount importance. According to Douglas Laney, author of the best-selling book “Infonomics”, most organizations today struggle with data inventory as their primary challenge. The key to establishing a robust data inventory is to simply replicate the well-honed asset management principles and practices that are already aligned with your core products or services- this gives a fundamental baseline to your data strategy.

2. Measuring and Valuing Data

Once you have your data inventory in place, it’s time to measure and value it. As the adage goes, ‘You can’t manage what you don’t measure’, and the same ideology applies to data points as well. In order to conduct an error-free measurement and valuation of your data metrics, the 5-P approach can be exceptionally beneficial- Purpose, Plan, Process, People and Performance. Take these factors into consideration while deploying your data analytics and data intelligence tools.

3. Monetizing Data

Data monetization is essentially the identification of recurring patterns from the available data assets that can generate quantifiable economic benefits. The typical roadblocks that organizations face include the lack of experience and skills to conceive, design, and implement data-driven recommendations. How can this be tackled? According to research, institutionalizing a Chief Data Officer with exclusive autonomy over budget and resource allocation is key to leverage your data assets and maximize its value in the long run.

4. Leveraging Data for a Better Customer Experience

Post the successful implementation of the above steps, it is time to focus on the convergence of customer experience (CX) and data analytics. An ideal customer experience constitutes the ability to engage with the customer in real-time, understanding their immediate needs, and providing that in the shortest stipulated time frame. This can only be achieved by analyzing the buyers’ journey throughout their brand interaction and tracking all the consumer touchpoints. “In essence, your CX strategy and data strategy need to function as interconnected cogs in the proverbial business wheel”, says Jonathan von Abo, Vice President of Partners and Alliances (EMEA) at MetaRouter. It is also worth mentioning that organizations that leverage customer behavioral insights and data outperform peers by 85% in sales growth and more than 25% in gross margin, as per McKinsey research.

Partnering for Success

The advent of cloud-based data analytics tools and platforms has democratized access to data, allowing businesses of all sizes to leverage the power of data and business intelligence. Subsequently, partnering with an experienced data analytics provider could be an excellent approach for organizations seeking a cost-effective and innovative data-driven solution. Connect with our expert data analysts to recognize the value of data-driven decision-making.


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