Traditionally, Business Intelligence (BI) projects have been viewed with trepidation because of the cost, time and resource requirements of the implementation. Much of this effort is connecting to data sources, then determining the appropriate metrics and configuring and maintaining multi-dimensional cubes to allow for slice/dice/filter type analysis of the data. It doesn’t have to be this way!
What if your BI solution auto-discovered metrics and the dimensional structure from within the data to be loaded? What if it then used this information to programmatically configure the dimensional hierarchy within the BI solution? What if it continued to perform this discovery operation and then synchronized the dimensional hierarchy immediately prior to each data load, including aligning with dimension structure changes? What if the dimension synchronization and data load happened immediately?
If you are in IT or a business analyst it would mean having more time to work on supporting the business rather than the ongoing effort of maintaining cubes and updating data loads. If you are a business person, it means less costs for IT maintenance, better support from IT and analysts, and fewer errors due to the automation.
And if you don’t have business analysts or a large IT staff? Even better as the automation means you won’t require them and can carry on with your business using the BI capabilities to improve decision making! This does require that you have intuitive, interactive analysis and analytic capabilities that are easy to use and consume – I’ll cover that in another post. If you are interested in knowing more, contact us.
There has been enough information and education about the benefits of cloud computing that most companies have considered or are actively moving to the use of cloud-based applications. However, in many cases the costs of moving to the cloud for core applications (ERP, MES, SCM etc) are significant and the migration can be disruptive to business operations. As an alternative we are seeing more companies adopting a hybrid approach – that is they retain their legacy on-premise core systems and augment these with cloud-based performance management, planning, information access and business intelligence.
This delivers the benefits of cloud-based ubiquitous access and ”new generation” capabilities such as mobile support, advanced visualization, interactive analysis and sophisticated analytics to a broader audience, without the trauma of a “heart transplant” for core transactional systems. Of course there are integration requirements the new capabilities must address and these will be described in future posts.
What are your thoughts? Are you delaying the move to cloud-based core systems? Does it make sense to leverage your existing on-premise systems and extract more value by delivering information access, planning and visual analytics to a broader audience to help them in their daily operational decisions?
I just obtained a copy of the new book from Steve Blank and Bob Dorf, “The Startup Owner’s Manual” and it is a great read for anyone working in a startup company. They have provided a lot of great information and given my interest in business intelligence, analytics and performance management, I honed in on the topic of “Get Ready to Sell: Build a Metrics Toolset”. This discusses selecting the right metrics to measure, being careful to keep the number of metrics, small, relevant and that tell a story, and presenting the information in a dashboard.
I was surprised and very happy to see a screenshot of myDIALS on page 343 as an example of a metrics dashboard – it was taken from our Online Marketing Module. This is great visibility for us at myDIALS and we are very grateful. More importantly, the book highlights the importance of metrics in cost-effectively scaling a business. Building a startup company is tough (I know from personal experience) and trying to do this without the ability to visualize, report and analyze appropriate metrics makes it much harder.
What the static screenshot doesn’t show is the capabilities that turn those metrics into real insight and actions:
- the ability to interactively filter, drill into the data, understand the formulae used to calculate the metrics etc
- alerts that proactively notify you of issues you need to address now, or warn you of potential issues on the horizon
- analytics such as trends, forecast projections, variance analysis, control charts, histograms and correlations that can provide insights derived from the metrics
- what-if scenarios to see the impact of potential changes before you take actions.
The book suggests hiring a “data chief” to manage the data and its interpretation, and to quarterback the company’s plan to use that data to drive continuous improvement. This is one area I have a different opinion to the authors. While a data chief can certainly help manage the data, metrics should be available to everyone in the organization making daily decisions. If equipped with the capabilities I’ve outlined above, each person can interpret the data themselves and directly drive continuous improvement, which I believe is far more effective and scalable as the company grows. What do you think?
Many organizations use Key Performance Indicators (KPIs) to measure business performance and results. In order to present a concise number of metrics that can be quickly scanned and evaluated, many of these KPIs are calculated metrics, for example:
- Gross Margin
- Productivity per head
- % Quota Achievement
- On-time In-full Order Fulfillment
- Overall Equipment Effectiveness.
Some of these calculated metrics (there are many other examples in addition to those listed above) are quite clear, consistent and well understood. However there are nuances in many that means different people could interpret them differently, for example: Read the rest of this article…
While business analytics can deliver valuable information and actionable insight to improve decision making, it can be hard work to ensure the data used for analytics is consistent with the source transactional and operational systems. This is because data is typically extracted from the source system, transformed and loaded into an analytic, multi-dimensional data store to empower users with the ability to rapidly ask questions and slice/dice/filter data to quickly get to the core of an issue. This raises some challenges to ensuring data consistency due to:
- latency introduced by having to move data into a different data store before it can be analyzed;
- inconsistencies in data structure whenever there is a structural change to the source system (simple examples are adding or removing customers or products, or changing the organization structure);
- delays in making the analysis services available to new users of the underlying systems.
To address the first point, source data has to be loaded into the analytic data store as frequently as required for good decision making; which can vary from immediate (triggered by a change in the source system) to every few minutes, every hour, every shift or every day. The legacy approach of crunching cubes over night or on weekends introduces significant latency and is not suitable for most of today’s operations.
The last point can be eliminated by using a central user information repository, user synchronization and single-sign-on, and these capabilities have been available for some time.
The middle point is very important as most organizations change frequently, not in terms of daily re-organizations although most companies seem to re-org every several months, but certainly in terms of new customers, products and employees. If the analytic system has to be manually re-configured each time these changes happen, it will quickly become a maintenance burden and probably lead to data inconsistencies. The answer here is the metadata contained with the source systems or a master data management system that can be extracted by an intelligent connector layer and used by the analytic system to auto-configure and dynamically synchronize the data structures and dimensional hierarchy. Not only does this ensure data consistency it eliminates the maintenance burden thus reducing costs.
Before implementing a business analytic system, it would be helpful to explore the three points above to ensure you will not incur significant maintenance overhead and that business decisions will be made on accurate, consistent data.
There has been a huge amount of interest and discussion around “big data” lately in the media and by analysts. As technology evolves, it is possible to hold much greater volumes of data and to apply visualizations and analytics to that data. As with anything, just because we can do something, it doesn’t necessarily mean we should do it. So the question I’d like to pose is what data should we store and analyze? This leads to a further questions regarding what data is valuable and how do we quantify the value of data?
It seems to me there are several aspects that affect data value:
- relevance;
- importance;
- timeliness;
- granularity; and
- age.
Read the rest of this article…
I was discussing performance management requirements with a potential customer the other day and it turned into an interesting conversation regarding the best way to approach performance metrics. They had been looking at the situation by starting with what data they had access to and then determining who might benefit most from access to that data. I’d describe that as an “inside-out” approach and as the conversation evolved we moved away from that approach to more of what I’d view as “outside-in” approach to performance management.
This approach can be summarized as: Read the rest of this article…
Whenever the use of business technology is covered in a mainstream publication, you know it is on the verge of broad adoption. So it was with interest that I read an article in the Wall Street Journal titled “So, What’s Your Algorithm?” that highlights the growing use of real-time analytics in the business world. In summary it outlines the how analytics harvested from massive databases will be used to inform day-to-day business decisions. I really like the quote that “Over time, this will change your world more than the iPad 3″ – not that I’m adverse to using my iPad.
Although the focus of the article is on analytics associated with big data applications, there are many applications for real-time and near-real-time analytics. These include aspects relating to more efficiently conducting business transactions, including: Read the rest of this article…
In the world of business intelligence and analytics, we sometimes run into a mis-understanding about what information is best presented in a tabular report versus information that is best analyzed using an interactive, analytic dashboard. It comes down to the characteristics of questions and answers that are required to enable actions or support decisions. Reports are great for static, single-question snapshots of a list of items. For example, if you need the following information, a tabular report generated from your transactional system is probably most appropriate:
- A list customers that placed an order in the last 5 days;
- A list of all students currently enrolled in a particular class;
- A list and quantity of products currently in backlog;
- A list of customers with payments outstanding for more than 60 days;
- A list of opportunities scheduled to close this week.
Another way of looking at this is that reports provide “what is” and “who are” type answers that are used to take tactical, operational actions.
Read the rest of this article…
In a recent post, I described the power of Visual Analytics, so it was gratifying to see a new report by David White of Aberdeen titled “Agile BI” that investigated the benefits of visual/interactive BI. In his survey with more than 200 respondents, David found that those using visual/interactive BI as opposed to traditional BI or a combination of both had:
- greater ability to get information on time;
- increased self-sufficiency and a greater percentage of power users;
- that users had greater capabilities to drill into detail, filter and explore data and tailor their experience.
This certainly mirrors what we perceive in the market and the capabilities we strive to deliver. Previously I have described the need for BI to operate at “business speed“, personalization and the concept of interactive investigation of data. All of these are key to delivering the requirements for rapid business insight within today’s organizations. As Aberdeen says, the decision window is shrinking which presents a challenge to IT departments and visual/interactive BI addresses this challenge and increases the business’ ability to meet the shrinking decision window.
