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Starting your Digital Analytics Journey?  
Starting your Digital Analytics Journey?  

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 “Give me six hours to chop down a tree and I will spend the first four sharpening the axe.” - Abraham Lincoln.  

Many people nod their heads when they hear this quote; but most don’t realize its importance. Besides personal development, this quote is critical at an organizational level as well. Preparation is essential; analyze, prepare and then execute. Today, organizations are sharpening their axes by incorporating data into the lifeline of their modern-day businesses, and you can do it too.  

It’s all about uncovering hidden opportunities and preparing your decision-making for the future using analytics. However, if data isn’t accurate or accessible, poor decisions are made. In a 2021 report by Experian, 95% of business leaders reported a negative impact to business due to poor data quality resulting in negative customer experiences and loss of customer and organizational trust among other effects. The demand for better decision making with data means that a comprehensive data strategy is no longer a nice to have. It needs to address challenges and questions around improving availability, timeliness and quality of data etc.  

According to New Vantage Partners' latest survey 84.6% of respondents held the role of Chief Data Officer, Chief Data & Analytics Officer, and reported that analytics is now formally a part of the CDO/CDAO role at 69.4% of organizations. However, a meager 23.9% of companies reported that they have created a data-driven organization. Analytics can be applied to various areas in an organization be it tactical, operational and even strategic. It gives a competitive edge — resulting in cost reduction, forecasting and predicting, and identifying new opportunities, markets and industries, as well as new products and services etc. determining pricing models and budget allocations.  

In order to kick-start your analytics journey, remember, your data strategy which focuses on two main pillars: 

  1. How to create data infrastructure?  

By focusing on data availability, quality and better management of data, which includes MDM and other data engineering applications, an organization can build a robust data infrastructure. Choosing where you will store the data, cleaning the data and optimizing its quality, building an EPL pipeline as well as taking care of data governance are all areas of focus. 

Data infrastructure consists of data assets and processes which is key in transforming your data into usable insights. You must keep in mind data accessibility and volume while constructing the data infrastructure. A solid data infrastructure paves the way for a smooth road of data efforts. Before you start with analyzing your data keep in mind who the internal stakeholders are and how they will employ the insights. Structed information is easier to interpret and results in measurable success. 

 

 

Image: Enterprise Data Management 

  1. How to make your data a strategic asset?  

Once you have the right data infrastructure in place, you can be confident of the data quality and making data a strategic asset for the enterprise. That further gives ensures that you derive intelligence from the data, with the help of advanced analytics tools. With the power of AI/ ML you can build enhanced datasets and define a data structure to customized to your needs among a host of other benefits. Advanced analytics and AI ensure that an organization manages their enterprise data and transforms it into an asset. 

 

 

Image: The Data Lifecycle 

So, do you have the right data infrastructure ready for your enterprise? 

In order to build a mature data infrastructure that scales, first ask yourself these questions.  

  • Are you capturing the right data?  

  • Do you have the data flow automated from different systems? (Without manual intervention) 

  • Do you have data governance processes put in place to manage data? 

One of the biggest challenges is building a data stack that grows with your business. To do so, it is important to keep in mind a few guidelines; identify the master schema for data elements, choose a central location/ single source of truth for users and slowly expand your data infrastructure over time instead of all at once. Once this is in place, identify the right tools for data infrastructure automation. Start small and then move onto exploring complex scenarios. For example, visualization tools can be impactful in bringing metrics to life. The business requirements and goals will determine which tools work best for your teams.  

It doesn’t stop here – it's important to also consider how you will keep scaling your data initiatives in an agile and reliable way. Here’s how;  

  • Continuous monitoring – this helps detect issues when something goes wrong.  

  • Automating workflows – this helps with data visualization, business intelligence and automated checks for data governance instead of manually updating data sources. 

  • Using raw data – it is useful to have raw original data stored in case something gets deleted/ lost.  

What sets a successful business apart is when they have developed the ability to convert their data as a strategic asset across the enterprise and incorporate a transformed approach of working with data. A data-first mindset is all about understanding that you can’t look at data in silos. For example, attracting and retaining talent isn’t just the HR functions’ problem – rather a company-wide issue. Building the right data-first culture helps optimize priorities and treat data as an asset across the entire company.  

A data infrastructure helps access critical business information, protects against losing insightful information, promotes collaboration, streamlines data governance and helps modernize legacy systems. It all boils down to creating value with data.  

 

Author: Laxminarayanan G - Senior Vice President & Global Delivery Head at Polestar Solutions 

 

 


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About Polestar Insights Inc. Founded in 2012 by Chetan Alsisaria (CEO & Co-Founder), Amit Alsisaria (COO & Co-Founder) and Ajay Goenka (CFO & Co-Founder), Polestar, is a leading AI & analytics solutions company that serves Fortune 1000 companies, startups and the government across various industries, including CPG & retail, manufacturing and pharmaceuticals, among others. Headquartered in Dallas, Texas, the company enables businesses across North Americas, Asia Pacific, ANZ, and the UK with analytics foundation, data science and AI initiatives, offering a comprehensive range of services to help succeed with their data. For more information visit: https://www.polestarllp.com/

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