3 Keys to building a data-driven strategy

Harish Thyagarajan

30 Nov 2018

keys-to-building-a-data-driven-strategy

Big data and analytics have come a long way and are now on top of the corporate agenda. A large number of entrepreneurs are depending on it to transform their businesses. At present, data-driven strategies are becoming an increasingly crucial point of competitive differentiation.

 

To successfully exploit data and analytics, companies require 3 supportive capabilities. First, a company must be in a position to identify, collate and manage many sources of data. Next, they should have the capability to set up an advanced analytics model to predict and optimize outcomes, and then the management must have the capability to transform the company so that the data and models can yield better decisions.

 

Two critical features underpin these competencies. a) an effective strategy on how data and analytics can be used for competing and deploying the right technology architecture. More importantly, a proper vision of the expected business impact has to shape the approach to data sourcing and the transformation of the business. Entrepreneurs must invest a lot of time and effort in aligning managers across the organization in support of the mission.

1. Picking the right data

The way data modeling is carried out has changed over the years. The amount of information available is rapidly growing, while the potential to expand insights by bringing together data is accelerating. Large sets of data offer companies a granular view of the business environment. The ability to see what was previously unavailable helps in improving the operations and customer experiences. This also means that you are doing well in 2 areas.

Most often, companies have all the data they need for tackling business challenges, however, managers just do not know the way they can put that into use effectively to make some serious business decisions. Companies must encourage their employees to understand data better by being specific about the business challenges and opportunities that they need to address.

 

Entrepreneurs must also get creative in exploring the external sources of data. The social media channels generate terabytes of unstructured data in various content forms. One way to bring in a broader thinking about the potential data is to find out if one can make business transforming decisions if all the information required is available at fingertips.

Legacy IT structures may hamper the new types of data sourcing and analysis. Existing IT architectures may not allow the integration of siloed information, and managing unstructured data often remains beyond traditional IT capabilities.

 

It will take many years to completely solve these issues, however, business leaders can address short-term needs by prioritizing the requirements. This will help in quickly identifying and connecting the most critical data for use in analytics and then setting up a cleanup operation to synchronize data and work around missing information.

2. A model to optimize the business outcomes

Although data is necessary, the performance improvements and the competitive advantage arise from the analytics models that help managers in predicting and optimizing the outcomes. More importantly, an effective approach to building a model generally begins with not just data but determining a business opportunity and how a business model can improve the performance.

 

Even though advanced statistical methods make for better models, experts at times design models that are way too complex to be practical and may even exhaust the organizational capabilities. Companies should look for the least complex model that can improve its performance.

3. Redefining the business capabilities

One of the major concerns of the top management teams is that they really do not understand big data models and thereby do not use them. These kinds of issues often pop up due to a mismatch between a company’s current culture and the emerging tactics to explore analytics.

 

The new strategies either do not sync well with how businesses arrive at decisions or completely fail in providing a blueprint for realizing business goals. In order to effectively use big data, your business has to go through a drastic change, and certain actions will lead you there.

A lot of companies fail in the implementation of big data and analytics because they are not in line with a firm’s day to day processes. Model designers will have to understand the kind of business judgments that managers make in order to align their actions with that of the company goals.

 

Interactions with top-level managers will ensure that the analytics and tools complement the current decision processes, so the companies can manage a range of trade-offs effectively.

A good number of companies must upgrade their analytical skills. In order to make analytics a part of day-to-day operations, managers will have to see it as central to sorting issues and also identifying good opportunities. The efforts put in will definitely vary based on a company’s goals.

 

Adjusting culture and the mindsets generally require a multifaceted approach, which includes regular training and role modeling by top-level managers or leaders. Usually, the forum/field approach works best, where the employees participate in the real-time analytics based workspace, where they get a real-time experience in learning more about analytics and its impact.

 

According to industry experts, this is the right time to invest in learning and implementation of big data and analytics.  However, instead of taking a big step forward, the leadership should focus on targeted efforts to source data and transform the company culture. Such efforts can help in maintaining flexibility.

 

As more number of organizations master the key skills of using big data, building superior capabilities will become a decisive competitive asset.


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