How to overcome challenges of Data Adoption in 2022

Making instinct-based decisions has uncertainties associated with it, thus leading organizations to explore ways to minimize risk. In an attempt to maximize the predictability of a decision’s outcomes, companies are now depending highly on data-driven decisions. While customer acquisition increases by 23%, the use of big data also helps businesses to increase the overall profit by 8% (BARC research).

Since data is offering a multitude of information, organizations are struggling to leverage its full potential. The topmost big data strategy is to identify the requirements of the business and then accordingly create the infrastructure to support and seize business opportunities. Following are some of the major challenges companies are facing when it comes to data adoption:

1. What problems businesses are trying to solve?
2. Does the company have the skills and infrastructure to leverage data?
3. How to democratize and demystify the data?

As per a Harvard Business Review article, firms that identify themselves as data-driven have come down to 31% (2019) from 37.1% (2017). This declination came despite the fact that there has been an increase in AI initiatives and big data investments.

Data Interactions up by 5000%

Between the years 2010 and 2020, the data interactions (creation, copying, capturing, and consuming) have increased by 5000%. Also, the usage of data significantly increased from 1.2 trillion GB to around 60 trillion GB. Adoption of work from home contributed to this rise in data interactions. However, the organizations are failing to analyze and utilize the data for driving insights. As per the study of IDC Digital University, only 0.5% of the entire data has been analyzed

in the year 2012. This is a matter of concern for many companies as they are struggling with identifying the proper use cases.

The Road to Digital Transformation and Data Adoption

As per a survey conducted, 77% of the CEOs held pandemic responsible for speeding up the company’s plans for digital transformation. Satya Nadella, the Microsoft CEO, mentioned that the company has witnessed two years’ transformation in just two months.

It has been found that if data adoption is less among C-Suite, the entire transformation fails in the initial part. The low adoption of data among C-Suite results in weak execution, thus resulting in thin data adoption across organizations. To address the issue, C-Suite needs to devise a strong data science strategy. If and when the company decides to leverage data, the adoption needs to first start from C-suite and then spread across different levels. This will instill confidence among employees across levels and help them to smoothly accept the changes.

Major Challenges Faced by Companies during Data Adoption

When it comes to Big Data, companies not only lack the infrastructure to support data processes but also have insufficient human resources. Following are some of the major big data issues companies are facing:

1. Lack of learned professionals
2. Lack of Big Data understanding
3. Issues with the growing data interactions
4. Choosing the appropriate tools for Big Data analysis
5. Integration of different data sources

Overcoming the Challenges

Taking one step at a time and brainstorming how to adopt data for digital transformations is the most effective way to overcome the challenges. However, in addition to overcoming the existing challenges, the companies must also focus on building the strength for future developments. Following are some of the ways with which companies can overcome major challenges posed during data adoption:

1. Defining the problems precisely to narrow down ways for analyzing data and driving insights. Big data will be more than 180 zettabytes by the year 2025. And it will continue to grow. However, from a company’s perspective, not all data is relevant. Hence it is crucial to narrow down the quality, quantity, and source of data for thorough analysis. This is precisely why a company must define the problems that they are trying to solve with data. The company’s data science strategy is its first step towards digital transformation. Whether it is for customer acquisition, customer retention, profit maximization, etc. the goal needs to be predefined and all the other processes must be aligned to it.
   
   
2. The appropriate skills for successful data transformation have no alternative. In a data-driven organization, the need for learned professionals is of paramount importance. Programming, data cleaning, data analytics, visualization, etc. are some of the many skills required by companies to embark upon their data journey. The biggest companies in the world including but not limited to Facebook, Amazon, and Google are highly data-driven and use different algorithms to maximize profits, minimize risk, and improve customer satisfaction. These companies invest heavily in hiring skills as well as upskilling their existing employees to make the best use of data. From Data Science Certification courses to welcoming the best Data Science talent in the team, these companies ensure to have covered unparalleled Data Science strategies.
   
   
3. Entire business teams must be in line with the company’s data adoption goals. While it is crucial for data to be first adopted by C-Suite, it is also required for the entire team to practice data adoption. The practice flow from top-to-bottom must be explicitly explained to the existing talents of the company. Since data science is in its early phase, the scope of improvement is huge. In addition to hiring data science talent to fill up the data science jobs opening, the company can also leverage the potential of data science certifications. There are hundreds of data science certification courses available. These courses can be completed by individuals at their own pace and help them contribute better to the company’s data adoption and digital transformation goals.

Conclude

The company’s attempt to leverage data for optimizing its day-to-day operations has unlimited benefits, however, the first step is to address the challenges that are posed by data adoption. From hiring top data science talents to upskilling existing employees by offering them data science certification courses, a company will surely see a successful transition and transformation.