Keys to Building a Data Driven Strategy

There is a lot of excitement surrounding trendy terms like big data, the Internet of Things, Artificial intelligence, analytics and all other cool trendy terms in the realm of data. It is by no fluke given that together, things like big data and analytics are transforming businesses around the globe but most enterprises still struggle with poor data quality meaning that they still cannot reap full benefits of data technologies.

 

data-driven-strategy

Well, research has shown that the route to evading data related issues requires three mutually exclusive supportive capabilities. First, an enterprise has to harness, blend and manage data from an array of sources.

They also need to create advanced analytics models for predicting and optimizing outcomes. Moreover, the enterprise has to familiarize with the data in a bid to yield better decisions. If you are lost then do not worry, as this article will take you through all the details of steps needed to build a successful data-driven strategy.

  1. Find the Right Data

The world today is filled with lots of data and the volume is escalating every day. Talking of social media data, open source data, sensor data, geo spatial data, and more that can afford enterprises panoramic and granular views of the business ecosystem. For example, an energy company can deploy smart meters connected to the Internet of Things (IoT), and get insights on consumer patterns. In simple words, this means that you now have the opportunity to improve your customer experience, operations, and strategy.  There are two ways to make good use of data around you:

Be Creative with Data

Many companies already have the data they need to drive in place but they simply do not know how to convert the data into information used to make decisions. Ironically, one of the impediments in big data and analytics might be in the management! For example, an operation executive might not see the value in daily and hourly customer service data in an enterprise. The way around this is to look at things from the perspective of enterprise’s challenges then doing an analysis of how data can be used to solve the problems.

It is also vital to look for smart ways to source external and new sources of data. For example, you can go for social media as a source where you can take advantage of terabytes of non-traditional and unstructured data in the form of chats, videos, photos etc.  You can then add this to data fetched from monitored processes and other external sources like demographics, forecast etc.

Find the Right IT Support

Most IT infrastructures are not tailored to handle new types of data sourcing, storage, and even analytics. In the short term, business leaders can address big data needs by working hand in hand with data experts to lay down big data and analytics requirements. Such priorities will come in the form of quickly sourcing and connecting vital data for use in analytics followed by a clean-up process targeted at synchronizing and merging any overlapping data.

  1. Create Models that Predict and Optimize Business Outcomes

Data might be a key but you will need proper analytic models if your enterprise is to be able to predict and business outcomes it can help in improving performance and your competitive advantage.  A palatable way towards building such a model will start from identifying a business opportunity before moving on to find how the model can improve performance. This kind of modelling is fast while root modelling in practical data relationships, tend to be relatively easier to understand.

Bear in mind that any modelling task has its own risks in that even though advanced statistical concepts generate better models, they might turn out too complex to be practical. So, always pick the least complex model that can trigger improved performance in your enterprise.

  1. Change Your Company’s Capabilities

Effective adoption of big data in the enterprise will certainly demand organizational change. Think of it this way, most managers today do not understand or even trust models based on big data thus they do not use them. Such problems usually crop up from the disparity in organizational culture and the emerging tactics needed to leverage analytics. In other words, the new tactics do not conform to the way most enterprises make decisions. So, how can this be solved?

Create and develop Business-Oriented Analytics

One way to ensure that big data and analytic implementations take off is to design models that are aligned to broad enterprise goals. To achieve this, model designers should try to have conversations with front-line managers in the quest to align analytics and tools to existing decision processes.

Embed Analytics in Simple Tools

It is also very important to understand that front-line managers may not have the skill sets of statisticians and software developers. Thus, you will need to give front-line managers the right tools and platforms to help them in using new models and algorithms on a day-to-day basis.

Enhance the Capabilities to Tap into the Power of Big Data

Simple and usable models may be foundations of a data-driven strategy but it will not do the magic without the right levels of literacy and analytic skills within the enterprise. The idea is to inculcate big data and analytics into the “DNA” of the enterprise where managers can view it as being central to decision making.  Adjusting to this new approach will call for approaches such as a training, role modeling from leaders etc.

  1. Make Feedback Loop

Now that you have defined and prioritized your business goals, you can then re-evaluate your strategy based on your experiences. One way to do this is to keep track of database changes (perhaps through a data catalog) made along the way in case you need to refer to them later on. Another key part of reevaluation is to have frequent conversations with the team members about the project and progress.

Conclusion:

Communication and agility form the cog towards a successful data strategy whilst technology serves as the vehicle that aids in implementation. Always remember that it is not about the trending tool in the market but it is all about what you are trying to achieve in your organization.

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