How To Use Big Data To Achieve A Data-Driven Enterprise

As our world is built on information, we embrace this digital era. Bulk of data pours in every day, looking for someone to examine it.

Earlier data was less in amount and well- structured that made its processing fairly easy but now sheer volume of data is available that is unstructured, raw and complex to manage to get valuable insights from it. Data wrangling plays a crucial role in gaining value out of data here. Social media platforms, smart technologies, gauges, sensors, mobile devices, texts, videos are some of the examples of data sources.

As behaviors, personal habits, activities, and views are responsible for data production; it is likely to contain valuable and actionable insights that can feed decisions and helps businesses to formulate and implement more targeted and personalized programmes.

A large amount of unstructured data needs an advanced platform to collect, manage and analyze it and fortunately, big data analytics tools do this in the best way.

Data science is highly promising in transforming the way of doing business, granting that performance gains which were seen in the 1990s during the redesigning of core processes by businesses. But now data-driven strategies have gained momentum; they mean a great difference at the point of competitive differentiation.

It’s highly important to achieve a data-driven enterprise. The questions that you need to ask to know whether your business is data driven or not are:

• How many times do executives examine performance or operational metrics? Do they enroll dashboards while on-demand reporting?
• Does your business still rely on “gut feeling” decision-making models?
• How much time does marketing organization…Supply chain…finance take for ad-hoc reporting? Who is responsible for data analytics?
• Which section of your business has the best reporting capabilities? How broadly are reports shared?
• Are reports readable and understandable?
• Which data source does help you to make and implement strategic decisions? Is the data source persuasive?

For the desired business impact, it is important to have an integrated approach to data sourcing, the shaping of organizational transformation, and model building. It saves you from the common trap of starting by asking how can data help you. Hiring the right talent who have undergone data science training can have a good impact in helping your organization goals.

The below information will help you to become a better data-driven enterprise.

1. Selection of the right data

Over the past few years, data and modeling have changed vastly. With data multiplication every day, the opportunities to get valuable insights is also growing by combining data. A large amount of data with high-performance analytics give more panoramic and granular views of business development. The ability to do what was previously impossible empowers operations, strategy, and customer experiences which step you up in two areas.

Smart data sourcing

Mostly, companies have all the essential data to solve business problems, but executives don’t have the knowledge to put this information to good use to make critical decisions. For instance, operation executives might not sense the value of the daily factory and customer-service data they possess. A more comprehensive look at data to specifically address business problems and opportunities can give a wider view of its potential usage.

The higher authorities should motivate managers to get creative about the potential of new data sources.  There is terabytes of unstructured and non traditional data flow every day via social media platforms in the form of texts, videos, and photos. Further, the data is flowing in from monitored processes, sensors, and external sources ranging from demographics to forecasting. Asking this question, “what could we have done if we had all the data that we require?” will encourage broader thinking about potential data.

IT support is essential

Traditional IT structures which have turned into legacy now may hinder advanced types of data storage, outsourcing, and assessment. The siloed information may not integrate due to existing IT architectures, and unstructured data management often stays beyond capabilities of traditional IT. It takes years to sort out these issues fully. CIOs can help businesses addressing short-term big data needs that will help to set priority according to requirements. The idea is to quickly find and connect all the essential data for analytics use and then cleaning up to sync and merge overlapping data to unveil any missing information.

2. Development of models that optimize business outcomes

Analytics models can mean a difference in performance improvements and competitive advantage that help managers to predict outcomes and optimise current business processes. Typically, the building of a model does not start with data but with recognition of business opportunities and analyzing the power of model in upsurging overall market value. The hypothesis that leads to modeling brings faster results and feeds models in functional data relationships which managers can understand easily.

There are inherent risks associated with modeling of any type. No doubt modern statistical methods craft for finer models, however, statistics masters sometimes design models that are complicated to understand and adapt and may destroy existing capabilities of organizations. Senior executives repeatedly ask for the least complex model that can improve their performance with an edge in the market.

3. Capabilities need to be upgraded

We often hear from senior executives that their managers don’t completely understand the logic behind big data-based models which does not generate trust and stops them to use. The problems crop up due to the mismatch between present culture and capabilities of an organization and emerging tactics that impede successful exploitation of analytics. Either the new models don’t go in line with the methodology of decision-making by companies or not able to provide a blueprint that can help to recognize end goals of businesses. The tools are specifically designed for those who hold expertise in modeling rather than for a common businessman on the front lines, and for few executives, these models are adaptable to master their use—a reason for failure if businesses try to infiltrate the new methods in their operations.

Efficient use of big data requires thoughtful organizational change, and three actions can help you to get the most of it.

Stick to  business-relevant analytics

Initial implementation of bid data fails most of the time due to lack of synchronization with a company’s day-to-day processes. The solution is a conversation with frontline managers that will ensure sync between analytics and tools existing decision processes.

Integrate analytics into simple tools

The alignment with new models and algorithms on a daily basis requires transparent methods at managers end. The separation of the statistics experts from the managers who want to use the data-driven insights is the key here. This will give frontline managers intuitive tools and interfaces that will help them to perform their job in a better way.

Exploit big data by building capabilities

Not complex but even to use simple models, businesses will need to upgrade their analytical skills. Managers must consider analytics as central to identify opportunities to make it part of the fabric of regular operations.