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Business Intelligence offers valuable chronological details to organizations to sustain in this competitive industry even during complex situations. For instance, the purchase departments make use of BI for monitoring, selecting and negotiating with vendors; the customer service departments for problem identification; airline industry uses it to administer the status and functioning of their personnel and fleets, and so on.
While there are several advantages associated with BI, it has a limited ability to make predictions, forecasts and inferences on undefined facts and linkages. For example, making predictions on customer behavior, or the chances of fraudulence, or making recommendations on the best propositions in online transactions. This is why a majority of organizations enhance their BI practices to incorporate predictive analytics as well as data mining, in combination with best strategic reporting and forecasting practices along with enhanced operational intelligence and decision-making role. With the help of advancement in the capability of moving from insight to action, many reputed business organizations have begun to combining historical analysis with predictive analysis to understand what immediate measures need to be taken.
In the economic environment of today, it is the advanced analytics through which organizations can identify fraudulence before writing a check, refunding money or settling any claim. This is quite simple; however, even companies with good data management processes, practices and infrastructures accustomed for traditional BI reporting cannot handle cumbersome requirements or unpredictable workloads of operational analytics at all times. And the best part is that most of the activities related to establishing enterprise-class BI, such as creating consistent data, refined data quality and data integration, and rigorous systems government, are able to create a sound basis for advanced analytics. Nevertheless, what is always questioned is the methodology to use existing platforms and sustain predictable governance and performance without affecting the analysts’ ability of exploring, transforming and developing models and data in an ad-hoc manner.
Advanced analytical techniques onto data help in solving complex problems, such as the proclivity of customers to prefer one action to another, or predicting the select few customers out of the given list of customers who will not remain a consumer in the upcoming six months, and so on. Further, advanced analytics are more comprehensive as compared to data mining, because of which feature, the enterprises are empowered to optimize their efforts on the potential customers. It also offers a persuasive, significant advantage, through which the right customers can be enticed to whom the right products can be positioned for. With focused attention, even the customer base can be increased. Advanced analytics also lead to cost savings, as it also helps in identifying appropriate supply with no wastage in warehousing, etc. It involves merging of data and insight analysis in the present and insight into the future, whilst also allowing for customer management strategy and developing acquisition modeling. Its application may vary from one enterprise to another, as per the needs and objectives.
The future of advanced analytics can be envisaged in the latter playing a key role as follows
- There will be low need for specialized software for making a trend analysis.
- We can easily transit to true real-time decision making while transacting with customers.
- Analyzing unstructured data will emanate into enhanced scientific (e.g. CAT scan images), medical or even military applications.
Nevertheless, how can an enterprise face the challenges efficiently and turn them into opportunities to explore? The answer is advanced analytics.
- This requires making use of specialized technology, assimilate and organize data, and take a fresh look of how the enterprise can be run in an efficient way.
- Advanced analytics facilitates bringing together both business and analytic technology for refined insight and forecasting.
Following are the future BI trends
- Building a stronger base
- Volume of data
- Faster speed
- Cloud service model
- Using social media
Building a stronger base
Today’s business demands more from the IT department and hence, for an extended BI and Analytical support, for which a stronger base is needed to fund future requirements. The key elements may include MDM, data quality enhancements and strong BI platforms. Considering the Return On Intelligence (ROI), this requires more efficient, industrialized approach to BI services.
Volume of Data
The rise in volume, speed and variety of data paves way for new architecture that not only collects and stores data, but also uses it. However, the challenge is that volumes of data would also include many amounts of small data, including sales reports, mail messages, etc which can influence the performance. Therefore, look for technology solutions, such as data warehouse appliances in columnar storage, memory analytics and smart software solutions.
Faster Speed
Irrespective of the size of data, the BI user needs quicker responses from the BI environment, in terms of standard reports or navigating through data. To accommodate requirements of storage, processing and gaining insights from large volumes of data, real time BI architectures will take the place of traditional data warehouses.
Cloud Service Model
Cloud service model is in great demand by business enterprises for every kind of usage – whether temporary or permanent. It is basically the services such as reporting and analytics being provided through a managed environment basis a new business model.
Using social media
Social media, such as Face book, Linked In and Twitter are in vogue. They have become a trend as a part of one’s everyday life, whether for business or personal objective. And therefore, they can provide organizations with key information on customer’s perspectives. When combined with the actual behavior of the customer in transactional systems, it can offer wealth of information to the organizations.
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