In the era of digital transformation, to view Data as a resource or asset is still in an early stage. Although it is the perception of the same data that sets apart an analytics-driven company from its market rivals. Hence it is safe to rule out that data and analytics have become loci, around which market strategy revolves. This industry comprises of Data science products, in house analytics and data-oriented services.

However, those who have not yet adapted to this transformation or haven’t implemented data-led strategies, it can pose a critical challenge to retain market share. This failure in times of accelerating digital disruption can cost them success to the brands, that are already using data better and faster to transform customer experiences and discover, develop and deliver on new or evolved digital business models. Thus creating market stagnancy or exit.

So where lies the possible obstacles?

The process of switching is currently hindered by numerous problems. Let us examine common problems:

Data: Most of the time data is present at isolated locations i.e., scattered. Sometimes this data can be redundant, unrelated to the main topic. But the most common problem lies in wastage of unprocessed data as the company may not have enough skillset, resources to analyze them.

Legacy Systems: Some businesses have legacy systems within their organization. As a result, it is hard to upgrade to digital transformation. Organizations, like a prison, law firm, hospital, banks, etc. have records that date back to decades. Scanning through those files is tedious and time-consuming.

Security and Privacy Risks: The decision-makers are often plagued with the doubt of being susceptible to external attacks or hacks that may endanger information about their organization along with Business to Company (B2C) clientele and Business to Business (B2B) clientele.

Lack of Advice or Improper Consultation: Most of the organization are not educated enough to understand the importance of digital transformation, how to implement it at each level.

Absence of Community: A lack of community would mean that talent is disconnected and cannot be exploited as a whole. This will lead to the fragmentation of culture, experience, and expertise needed to reach the organizational goals community and brainstorm ideas to achieve them.

How to tackle the problem areas?

Data Analytics helps an enterprise in various manner. These include growth in revenue generated, quantifying cost drivers, identifying efficiency drains thereby boosting productivity and reducing operational losses and risks.

When the benefits are exponentially huge, why the delay?

Here are some steps that can help to mitigate the situation and further community building:

• Build a strong and open community by connecting with the isolated groups and skill tanks. This increased communication will foster minds to grow together with shared tools and techniques to address the questions posed.

• Have a clear vision and understanding. This will push for successful data-centricity across all facets of a firm or company.

• Gather Intel and based on it develop business-relevant analytics models that can be understood, give predictable outcomes and put to use. Then embed analytics into simple tools that can be utilized by employees with a basic level of skills.

• Practice Inter and Interdepartmental networking. Making use of multidisciplinary teams (like data scientists, engineers, developers, analysts) through the collaboration will help create a notion of positive change.

Even a reward system in the form of incentives, offers, etc. can act as a catalyst to bring forth the real spirit of the data-driven culture. At meetings, the teams with high performance should be praised while others can be encouraged and allowed to attend workshops that can focus on methods and scopes of improvement. It will indicate that the team leader values the skills and contributions of its analytics personnel and is committed to providing them with opportunities for professional development and growth.

• Data maintenance with sharing must be emphasized. This will ensure easy accessibility for the entire workforce and smooth functioning.

• Build, buy and borrow advanced analytics competencies (such as data science or machine learning) beyond traditional business intelligence and embed them throughout the business.

• Take measures to allow consolidation beyond the political and superiority barriers. Bridge the talent gap, break the fear of failure culture and avoid complex data architectures in beginning. After analytical literacy, develop capabilities to deal with big, complicated data sets. Still, if any problem arises don’t hesitate to take IT to help by outsourcing.

The journey to evolve from being data-aware to a data-driven organization can seem tough and stressful. On the contrary data and analytics have the potential to ease processing info, make informed decisions, have actionable insights, plan thoroughly and tap the best market has to offer. Getting enthusiastic at the prospect of a deliverable project, giving up on it when no visible impact is seen may not reflect well on the company nor at an individual level. The key is to be patient, learn and interact with communities for both short and long term benefits. Companies have to understand the vitality of analytics and invest in the tools and hire data scientists to seize this opportunity.