By 2018, more than half of large organizations will compete using advanced analytics and proprietary algorithms. Despite this, data analytics is sometimes seen by businesses as being too complicated. Simply put, many businesses see analytics as being too costly, time-consuming, and inaccurate to justify more than a token effort at implementation.
However, with more and more data being generated everyday, roughly 2.5 quintillion bytes of data daily, many organizations are discovering that they can gain a real advantage over their competition simply by mining any and all available information for useful insights. As such, those that continue to shun business analytics as ‘unnecessary’ may find themselves falling further and further behind their more data-driven competition.
The truth is that data analytics, particularly in regard to how data stores are being managed, can play a significant role in an organization’s success.
Data analytics are designed for businesses that want to make good use of the data that they are taking in. Businesses that can use data analytics properly are more likely than others to succeed and thrive. But with all of the advantages of data analytics, the key benefits can be described in this way:
- Data analytics reduces the costs associated with running a business. This is thanks to the business’ increased understanding regarding particular trends found in the data being reviewed.
- Data analytics cuts down on the time needed to come to strategy-defining decisions. As conclusions are mined from the available data, these conclusions are presented in a clear and accessible way, allowing business leaders to quickly move forward.
- Data analytics help to more-accurately define customer trends. This information can be used in the creation of new products or services, allowing for better fulfillment of customer needs.
But despite the benefits of data analytics, it may simply not be enough. It’s becoming vital that forward-thinking organizations move beyond traditional analytics, and onto advanced analytics.
While it can be difficult to pin down an exact definition of advanced analytics, the idea is generally thought of as a more-complicated, yet also more-effective form of data analysis. This entails a deeper review of data in many forms.
More specifically, there are a number of factors inherent in the concept of advanced analytics:
- Data and text mining may be used to find specific trends or pieces of data.
- Visualization is used to gather existing information for the creation of visual images showing trends, comparisons, and other statistical points.
- Cluster analysis helps to take pieces of data that are similar to each other and separate that data from other groups. This is to facilitate effective comparisons.
- Simulations may even be generated to figure out what future trends or results might come about based on the statistics being reviewed.
- Predictive analytics uses techniques associated with data mining, machine learning, statistical analysis, and others to generate highly-accurate predictions about future business trends.
Advanced analytics specifically focuses more on forecasting, using the data it gathers to find the trends that can be used to determine what might happen in the future. In particular, a ‘what-if’ analysis may be generated by an advanced analytics program. A ‘what-if’ analysis is where values are flexible, allowing for the consideration of hypothetical data or circumstances. The ‘what-if analysis’ helps to see what might happen if something goes on over time, thus making it easier for a business to prepare for any uncertainty that might pop up.
Given the growing familiarity and popularity of data analytics, there are a number of advanced analytics programs available on the market. However, while each program offers big data solutions, not all are created equal. But while the programs themselves may offer different focuses and capabilities, generally speaking, the tasks that are expected to perform will be similar from one company to the next. As such, there are certain traits to look for in any analytics solution that will help you gage just how effective it will be in improving your business.
Perhaps the most important factor in determining how effective an analytics tool will be is usability. The tool itself will more-than likely be used across a wide range of individuals, departments, and systems within an organization, it will need to be designed for compatibility with a large number of platforms and other tools. Choosing a program that can function optimally across a variety of platforms can help promote collaboration between users. Often, in order for this to be possible, the program itself will need to be cloud-based.
Likewise, the interface itself will need to be intuitive enough for non-IT users to quickly master it. Data visualization makes it possible for large amounts of information to be shared visually, allowing users to quickly grasp important information, and built in tutorial programs (as well as other help features such as live-chat and telephone support) can help even the least tech-savvy of users learn to operate the system.
And even though the basic function of an analytics tool is going to remain very similar across industries, the organizations that employ those tools are often as different as night and day. No one business is exactly like any other, which means that no single, out-of-the-box analytics solution is likely to be a perfect fit for any particular organization. Advanced analytics software that can be adapted to fit existing systems and processes allows for easier integration and higher adoption rates.
As for the data itself, advanced data analytics programs that process data in real time (rather than as batches) provide users with the advantage of always being able to work with data that is completely current.
Advanced analysis solutions which feature these specific traits are much more likely to improve business functions, and result in a higher ROI than more-limited analytics options. That being said, few analytics tools offer the full range of functionality at a cost that is scalable to fit any business. In fact, the one exception to this rule is Salesforce.