Year after year the hype surrounding data analytics becomes louder. Thought leaders and research organizations have sung the praises of analytics as a means for generating much-needed insight into business operations, and companies that have embraced analytics have been able to translate insight to better operational productivity and faster, more accurate decision-making. In a competitive business environment where your competition is just as hungry as you are to reach and secure new customers and where business leaders need to make accurate, fact-based decisions about the company’s future, analytics can be the game-changer that makes the difference between success and failure. Here’s how:
The beauty of analytics is that it can serve as a guideline for transforming sub-par business performance to one that is efficient and profitable. Whereas reports provide a historical view of what transpired, analytic output is forward-looking, and plays an integral role in helping executives plan for the future.
Continue reading this post>>
While it might seem like every company on earth is using business intelligence tools to glean insight from their corporate data, surveys say that nearly 10% of companies do not yet have BI in place. Even though 91% of companies may have it deployed somewhere in their organization, anecdotally BI vendors like to trot out the statistic that only 20% of potential users have access to business intelligence. The more companies we talk to, the more this seems true.
If you run a company or a department that doesn’t have access to BI tools, you might wonder how you can use them. Boris Evelson from Forrester Research compiled a list of analysis types that may apply to your situation:
- Historical (what happened)
- Operational (what is happening now)
- Analytical (why did it happen)
- Predictive (what might happen)
- Prescriptive (what should I do about it)
- Exploratory (what’s out there that I don’t know about)
When you’re first starting out with BI, you’ll likely be most interested in historical and operational analytics, though we often work with finance teams who want to dive right into predictive analytics. Let’s look at a few practical BI use cases in various departments of an organization.
Finance Department: Historical, Operational, Analytical, and Predictive Analysis
Financial Transparency – Architecting Success
Recorded Date: December 10, 2013
Duration: 45 min.
Presenters: Dwight deVera, arcplan Senior VP of Solutions Delivery; Jeff Lovett, Teradata VP, Finance & Performance Management
About this webinar:
Too many finance organizations manage their data using people, processes, and systems that are separated from the rest of the organization. This walled-off ecosystem requests data from other areas of the company and produces its own analytics often with different definitions of the same metric e.g. (Revenue, Margin) that conflict with those of line managers. With the ever increasing pace of change in the business this siloed, duplicative approach to financial analytics cannot deliver a transparent, integrated view that serves both finance and operations. Defining a simple architecture optimizes data management and makes it easy to visualize joint opportunities across both organizations.
- Leveraging insight into financial results, drivers and KPIs to provide visualized, actionable views of financial performance
- Telling the integrated contextual story of a company’s operations through common views and analysis
- Ending reliance on averages for more accurate, behavioral measures of customer or product profitability
- Utilizing the next generation of analytical techniques to unearth trends and predict organizational performance
People do crazy things, especially during the holiday season. On Black Friday – and even on Thanksgiving evening – customers will wait in line for hours to grab the best deals and knock items off their wish list. Retailers offer ridiculous discounts on high-priced items and keep doors open 3 days straight to cater to buyers around the clock, then web retailers kick in their own Cyber Monday promotions. Black Friday has remained the number one shopping day for the past decade, accounting for most of the sales that businesses reign in during the holiday season. But smart retailers don’t have to wait until Black Friday to ramp up their bottom line. Your analytical or business intelligence platform can keep a pulse on operations year round and help increase sales before the 11th hour. Here’s how:
1) Use analytics for in-store promotions
Analytics can help guide store layouts by tracking which products perform best on an aisle shelf vs. an endcap in various cities, and what products should go on sale in a given month and region based on sales history and inventory levels. Using business intelligence, you can run scenarios based on historical data and make predictions about programs designed to drive in-store business. For example, if you increase the circulation of your direct-mail flyer, how much additional business can you expect it to drive? Similarly, if you offer in-store coupons for a certain timeframe, what kind of sales uptick can you expect? Essentially, you can analyze past customer purchasing behavior to determine how to best influence future purchasing behavior.
arcplan’s retail customers use our platform to track KPIs and run what-if scenarios to ensure that products are priced appropriately. For retailers, one important KPI is the cost of goods sold (COGS) – the price paid for the product, plus shipping, handling and other expenses to get it ready for sale. By keeping track of COGS…
Unearthing previously unimaginable insights from massive data sets is the premise of all the big data hype. Over the past few years as more and more stories come out about how companies are finding competitive advantages in their data, big data has moved beyond the buzz. Enterprises are deploying big data projects at a faster rate every year, and even more plan to do so within the next 2 years.
The extent to which a company can take advantage of big data analysis is determined by the amount of resources and infrastructure it has available. The good news is that now the barriers to entry have been lowered, making it possible for more organizations to meet their goals to transform operations with insights gained from big data. Here are three approaches that companies of any size can take based on their particular situation.
One thing to note is that these are underlying infrastructure approaches, and that you’ll still need an analytic engine like arcplan on top in order to interact with, visualize and distribute your insights.
Lots of resources and lots of infrastructure
Before big data was “big data,” Teradata was the only game in town. They’ve been at it for so long and their functionality is so robust – some of their capabilities are second to none. Now other vendors like SAP (with HANA) and Kognitio have their own massively parallel analytic databases. They offer robust processing and querying power on multiple machines simultaneously, enable near real-time MDX (Multidimensional Expressions, for OLAP querying) and SQL (Structured Query Language, the standard way to ask a database a question) queries, and in the case of SAP HANA and Kognitio, are fully in-memory. Not surprisingly, Teradata and SAP HANA come at a high price, but for that high price, the insights you achieve can be very near the speed of thought.