How companies leverage knowledge analytics to realize a aggressive edge
There is a good reason why businesses are adopting data-driven approaches to their operations.
According to Gartner, companies may lose their competitive advantage if they fail to accurately study the business potential of data analytics and tweak their business strategies to match.
Further, another prediction claims that up to 80 percent of enterprises will establish data management and monetization functions by next year, enabling them to enhance their business functions further, and create new revenue streams.
But being data-driven could mean different things to different organizations, and similarly, the benefits and advantage that they gain from it could vary from one business to the next.
Here are some the different ways companies are leveraging data analytics to propel their business to greater heights:
#1 | Optimize existing workflow
One of the immediate benefits that companies stand to gain from deploying data analytics is the ability to improve their current processes and workflows.
This could include gaining increased visibility of the supply chain, optimizing manufacturing operations, and even eliminating redundancy. These improvements could also be considered the lowest hanging fruits that pose lower risks with quicker returns.
Optimizing current workflows is perhaps also the easiest place to start, as companies embrace the transformation to become a ‘dashboard’ organization.
#2 | Discover new business models and revenue streams
Companies with new and innovative business models, with numerous revenue streams, are often valued significantly higher than those without.
Data analytics enables companies to explore and discover new business models that weren’t visible before.
Furthermore, forward-thinking companies in the digital economy are also increasingly monetizing their data, which is a vital differentiator in a crowded marketplace.
#3 | Find new sources of data for robust insights
When deploying data analytics, companies will inevitably gather more data, and perhaps, more importantly, look for new data at more unique places. Most often, they do this to build algorithms to power automation or derive valuable insights.
While algorithms are essential to data analytics deployment, it is essentially the data volume and diversity that builds more robust algorithms.
This is because the bigger and more diverse the data set are, the more accurate the insights would be. And by looking at a wide-ranging data set, the algorithms may find relationships it otherwise couldn’t with smaller data sets.
#4 | Design better IT infrastructure
As companies transition towards being data-driven, they also will come to understand how data is served, accessed, and processed at every step of the data management function.
To effectively and securely do this, organizations have to establish a proper IT infrastructure and implement strict security standards.
Further, training an algorithm is an iterative process, and more iterations will result in better insights, for which, proper, sufficiently robust infrastructures are needed.