IT Cost Cutting and Revenue Enhancing Projects

In the current economic climate the CIOs and IT managers are constantly pushed to “do more with less”. However, blindly following this mantra can be a recipe for disaster. These days IT budgets are getting squeezed and there are fewer resources to go around however, literally trying to “do more with less” is the wrong approach. The “do more” approach implies that IT operations were not running efficiently and there was a lot of fat that could be trimmed — quite often that is simply not the case. It is not always possible to find a person or a piece of hardware that is sitting idle which can be cut from the budget without impacting something. However, in most IT departments there are still a lot of opportunities to save cost. But the “do more with less” mantra’s approach of actually trying to do more with less maybe flawed! Instead the right slogan should be something along the lines of “work smarter” or “smart utilization of shrinking resources”; not exactly catchy but conveys what is really needed.

When the times are tough IT departments tend to hunker down and act like hibernating bears – they reduce all activity (especially new projects) to a minimum and try to ride out the winter, not recognizing the opportunity that a recession brings. A more productive approach is to rethink your IT strategy, initiate new projects that enhance your competitive advantage, cut those that don’t, and reinvigorate the IT department in better alignment with the business needs and a more efficient cost structure. The economic climate and the renewed focus on cost reduction provides the much needed impetus to push new initiatives through that couldn’t be done before. Corporate strategy guru Richard Rumelt says,

“There are only two paths to substantially higher performance, one is through continued new inventions and the other requires exploiting changes in your environment.”

Inventing something substantial and new is not always easy or even possible but as the luck would have it the winds of change is blowing pretty hard these days both in technology and in the business environment. Cloud computing has emerged as a disruptive technology and is changing the way applications are built and deployed. Virtualization is changing the way IT departments buy hardware and build data centers. There is a renewed focus on enterprise wide information systems and emergence of new software and techniques have made business intelligence affordable and easy to deploy. These are all signs of major changes afoot in the IT industry. On the business side of the equation the current economic climate is reshaping the landscape and a new breed of winners and losers is sure to emerge. What is needed is a vision, strategy, and will to capitalize on these opportunities and turn them into competitive advantage. Recently a health care client of ours spent roughly $1 million on a BI and data strategy initiative and realized $5 million in savings in the first year due to increased operational efficiency.
 
Broadly speaking IT initiatives can be evaluated along two dimensions cost efficiency and competitive advantage. Cost efficiency defines a project’s ability to lower the cost structure and help you run operations more efficiently. Projects along the competitive advantage dimension provide greater insight into your business and/or market trends and help you gain an edge on the competition. Quite often projects along this dimension rely on an early mover’s advantage which overtime may turn into a “me too” as the competitors jump aboard the same bandwagon. The life of such a competitive advantage can be extended by superior execution but overtime it will fade – think supply-chain automation that gave Dell its competitive advantage in early years. Therefore such projects should be approached with a sense of urgency as each passing day erodes the potential for higher profits. In this framework each project can be considered to have a component of each dimension and can be plotted along these dimensions to help you prioritize projects that can turn recession into an opportunity for gaining competitive edge. Here are six initiatives that can help you break the IT hibernation, help you lower your cost structure, and gain an edge on the competition:

• Server virtualization
• Enterprise application & data audit
• Cloud computing
• Business Intelligence (BI)
• Business process automation
• Centralization & automation of IT operations

Figure 1: Categorization of IT Projects 

In the current economic climate no project can go too far without an ROI justification and calculating ROI for an IT project especially something that does not directly produce revenue can be notoriously hard. While calculating ROI for these projects is beyond the scope of this article I hope to return to this issue soon with templates to help you get through the scrutiny of the CFO’s office. For now I will leave you with the thought that ROI can be thought of in terms three components:

• A value statement
• Hard ROI (direct ROI)
• Soft ROI (indirect ROI)

Each one is progressively harder to calculate and requires additional level of rigor and detail but improves the accuracy of calculation. I hope to discuss this subject in more detail in future blog entries.

Cutting costs should not mean cutting revenue.

Image courtesy of BusinessWeek 9/25/08: "AmerisourceBergen's Scrimp-and-Save Dave"

The financial panic of late has caused a lot of attention on cutting costs – from frivolities like pens at customer service counters to headcount – organizations are slowing spending. Bad times force management to review every expense, and in these times obsess with them. Financial peace however has two sides – expense and revenue.

A side effect of cost cutting can be stunted revenue, over both the short and long terms. It is easier to evaluate costs than to uncover revenue opportunities, such as determining  truly profitable offerings and adapting your strategies to maximize sales. Also as difficult to quantify are the true loses in unprofitable transactions, and competitive strategies that can negatively impact your competition.

The answers to many of these questions can be  unearthed from data scattered around an organization, groking customers and instantly shared knowledge between disciplines. For example, by combining:

• customer survey data;
• external observations;
• clues left on web visits;
• and other correspondence within the corporation;

…an organization can uncover unmet needs to satisfy before the competition, and at reduced investment cost.

When external factors, like a gloomy job outlook, cause customers to change behavior, it is time to use all information at your disposal. Those prospects changing preferences for your offerings can provide golden intelligence about the competition or unmet needs.

Pumping information like this is the heart of business intelligence. Marketing and Sales can uncover the opportunity; however, it is up to the enterprise to determine how to execute a timely offering. Financials, human capital planning, and operations, work in concert to develop the strategy which requires forecasting data, operational statistics and capacity planning data to line up.

A good strategist views all angles, not just reducing cost.

Designed to Sell, Corporate Edition

When contemplating which business units or product lines to put up for sale in today’s challenging market, it might be wise to borrow some tactics from  the real estate market. It really comes down to three important guiding principles in planning a divestiture as part of your deleveraging strategy:

1. Know your market – cultivate target buyers to avoid a fire sale. Identify players looking for complementiarity in products, services or customer base.

2. Model the outcome on your going-forward financials – freeing up cash may be top of mind for everyone, but we all need to think past the current crisis and understand what the impact will be on sales and profitability going forward. If you don’t have a business intelligence toolset in place already, you may have difficulty in achieving the type of agile scenario modelling that is necessary here. Infoworld is reporting BI as a key spending area in the recession, specifically for determining profitability.

3. Know where to invest, or “design to sell.” – there may be secondary benefits, above and beyond a divestiture’s products, services, and customer base. Specifically in the technology architecture, especially if the business unit is on its own (instead of shared corporate) platforms. Ancient mainframe technology is like the walnut panelling and avocado shag carpeting lurking in the basement. Customized applications with their big in-house support teams are like the pink stucco patio and poolhouse a proud homeowner showcases, causing the buyer to race down the road to the next listing. Call in the design team, these could be good spots to begin a corporate makeover, as they are very likely to increase the value of the sale.

On the flip side, things like collaboration tools and  business process management suites are like the well-appointed master suites and media rooms that can help a buyer warm up to the sale. In addition to things like a lean operating architecture, these technologies help make a divestiture an attractive asset for buyers looking to build out a platform company.

10 things you can do right now to improve your Enterprise Business Intelligence (BI) capabilities

1. Document examples of manulytics (manual analytics) activities to illustrate hidden fixed costs. Any BI investment initiative needs executive support and budget. You need to make a case for the investment to improve your BI capability and show the business a ROI (Return on Investment). The cost of the current manualytics activity needs to be documented to highlight the hidden fixed costs of the current way of doing business to help build consensus to make improvements.
2. Identify manualytics processes to be moved to production and automate.
3. Raise awareness of data as a corporate asset.
4. Enlist and cultivate a C-level executive sponsor for your Enterprise BI effort.
5. When the business asks a question that is difficult to answer – keep track of the level of effort expended to generate the information. How many analysts with spreadsheets are compiling information manually? When the answers accuracy are questioned, how much more time is spent proving the numbers are correct.
6. Development and document metadata wherever possible – Build in metadata requirements gathering into your SDLC – Create and standardize a process to capture table and column definitions and business logic into a standard format. Get tribal knowledge documented so that the business can continue to operate if people leave or move on.
7. Data Governance – develop a committee to work towards managing the data and IT assets of the organization.
8. Create/Assign data stewards for each of the source systems to agree on service level agreements for your source systems and resolve data quality issues.
9. Work to centralize your reference data – business hierarchies like department and product need to be centralized, agreed upon by all stakeholders – this is a task that can be driven by your corporate governance committee.
10. Don’t boil the ocean – Look for candidate pilot projects with a narrow scope to show quick wins to the business (90 day max.)
11. Work toward tool standardization – many organizations own one of each BI tool – work to standardize on one or two.
12. Build a Center of Excellence around BI and ETL – work to centralize your internal expertise for BI and ETL.

Well we ended up with twelve items, any one of which could fill a book or whitepaper and may be the subject of a future post.

As we work with different organizations, similar themes emerge. Every organization is different and your road to BI maturity is different from other companies. Sometimes it pays to have a fresh set of eyes come in and survey your current state to get you started on the right foot.

Business Intelligence: Avoiding “Operation Mincemeat”

What? Operation what? That’s right, Mincemeat and I am not referring to scrumptious pies at Christmas…..no I am referring to the reality of misinformation. What was a successful operation and asset to the Allies has become a minefield for today’s enterprise and a thirst, or more likely a downright need, for the ability to utilize our valuable data in a meaningful manner.

Let me digress for a second……I do not think we all need to be reminded of what Business Intelligence can do for us today – the facts and the benefits are plain and clear – take your data and make it actionable. Remove the idea of reporting on data and realize the vision of using data….

So one wonders why we have not all embarked upon our voyage of discovery aboard the great ship “B.I. Enlightenment.” And when we start to walk up the boarding ramp, waving goodbye to the stale data of yesteryear and the meaningless seventy characters of green bar reports we never understood anyway, we spare a moment to think “are there any icebergs in this sea?”

Of course, when one makes that pause there is a realization – what are you really gaining intelligence into? For many enterprises, our data is split across several systems and platforms; some of which are real time in nature others of which may be a week behind the times.

From what I have seen, many people are requesting more information on the tools – Which is best of breed? What do I get out of the box? Can my analysts use it? Can my dog understand it as he brings me my Sunday morning paper? How quickly can you show me my data in action?  These questions can all be answered and the “wow factor” of BI can take precedent and the definition of KPIs ensues full steam ahead for some people.

What is missing? I appear to be on the first landing but I do not remember taking the first flight of stairs…….well let’s flip back to “Operation Mincemeat”: or now to be known as “Is my data ready for Intelligence?” A critical step that must be considered lies not in the value of BI but in the readiness of your data. Misinformation was wonderful in the 1940’s but it has no place in the business arena.

When your data becomes an actionable entity you must be able to rely on its accuracy and ease of access. Reporting on reports was the way of the past – “That looks great Bill but can you cross reference that with data store “x” as sometimes we can be a little stale”. The true key to embarking on Business Intelligence is understanding where you are today in your data maturity and most important, how do you get where you need to be – reliable, reusable, actionable information.

So what does it all mean?  It means, assess where you are before you set sail……the voyage is glorious and the sights not to be missed but make sure you have a ticket for the right ship.

What is Manualytics and why should I care?

Manualytics (manual analytics) is the labor intensive, manual process of creating information. It involves finding, loading, correlating and consolidating data into spreadsheets to answer a particular question.

The business leadership asks a question which initiates a number of analysts to start sift through the silos, usually with spreadsheets.

The answers from different departments don’t agree. That spawns a second round of analysts with spreadsheets trying to prove whose numbers are correct.

The business is left with an answer they don’t trust which leads to decision making with inordinate risk.

Does this sound familiar? Can you give examples in your organization that resemble this process? You are not alone. Manual analytics exists in virtually all organizations because the business can create questions faster than IT can provide answers.

The problems with Manualtyics include:

• It is inefficient and labor intensive
• It produces inconsistent results and can compounded errors
• It buries complex business logic in spreadsheets
• It introduces uncertainty and confusion
• It engenders mistrust of the data
• It leads to risky decisions
• Second and third layer of analysis
• Data Untraceable from Target to Source – compliance anyone?

One of the largest problems is that Manualytics is a hidden overhead cost/activity in many organizations. If the manual spreadsheets (or any desktop system) are run every month and are business critical – then they need to be productionalized and automated.

If you don’t have a program to improve your BI capabilities and limit/reduce the amount of manual analytics then you are treading water at best.

This siloed architecture and manualytics activities describes what we call a typical BI current state. We see this situation to varying degrees, in one form or another, in virtually all organizations.

So how do we break out of this cycle?

How do we position our systems and people to obtain actionable information?

How do we overcome our siloed architectures and maximize our long term IT investments? 

Why creating actionable information from your existing systems is so difficult

With all the easy to use business intelligence tools and technology we have today, why is it so difficult to create actionable information from the wealth of data in our organizations?

One needs to understand, at a high level, the systems we have built and how they got that way. Your core business systems have evolved over time, budget cycle by budget cycle with no eye towards the overall enterprise. Systems were built to support core business functions – Payroll/HR, General Ledger, Inventory, etc. They were transactional in nature; designed to meet the immediate requirements (e.g. cut payroll checks, track inventory, manage an assembly line, etc.) which did not include getting business intelligence out. Over time these systems became islands of data, popularly known as silos.

Add the fact that silos are structured differently and common data like product and customer is typically not standardized, answering questions across silos is difficult and labor intensive.

As these systems matured, the owners of each silo had departmental Business Intelligence needs. So as budget became available they added a data warehouse or data mart on top of their silo and created something like this.

The result is larger silos with larger sunk investment and still no ability to provide enterprise answers or actionable information. This approach worked for immediate departmental BI needs but if the business asks a question from data that resides in two or more of the silos, getting the answer usually involves a significant IT effort. By the time IT responds the business has gone onto a different question. The business analyst starts gluing spreadsheets together to provide some insight kicking off the next activity in the BI food chain – manual analytics.

Why Analytics Projects Fail

During an informal forum recently, (whose members shall remain nameless to protect my sorry existence a few more years), analytics projects came up as a topic.  The question was a simple one.  All of the industry analysts and surveys said analytic products and projects would be hot and soak up the bulk of the meager discretionary funds availed a CIO by his grateful company.  If true, why were things so quiet?  Why no “thundering” successes?

My answer was to put forward the “typical” project plan of a hypothetical predictive analytics project as a straw man to explore the topic:

• First, spend $50 to $100K on product selection.
• Second, hire a contractor in the product selected and tell him you want a forecasting model for revenue and cost. 
• The contractor says fine, I’ll set up default questions, by the way where is the data?
• The contractor is pointed to the users. He successively moves down the organization until he passes through the hands-on user actually driving the applications and reporting (ultimately fingering IT as the source of all data).  On the way the contractor finds a fair amount of the data he needs in Excel spreadsheets and Access databases on the user’s PCs (at this point a CFO in the group hails me as Nostradamus because that is where his data resides).
• IT gets some extracts together containing the remaining data required that seems to meet the needs the contractor described (as far as they can tell, then IT hits the Staple’s Easy Button —  got to get back to keeping the lights on and the mainline applications running!).
• Contractor puts the extracts in the analytics product, does some back testing with what ever data he has, makes some neat graphics and charts and declares victory.
• Senior management is thrilled, the application is quite cool and predicts last month spot on.  Next month even looks close to the current Excel spreadsheet forecast.
• During the ensuing quarter, the cool charts and graphs look stranger and stranger until the model flames out with bizarre error messages.
• The conclusion is drawn that the technology is obviously not ready for prime time and that lazy CIO should have warned us.  It’s his problem and he should fix it, isn’t that why we keep him around?

At this point there are a number of shaking heads and muffled chuckles; we have seen this passion play before.  The problem is not any product’s fault or really any individual’s fault (it is that evil nobody again, the bane of my life).  The problem lies in the project approach.

So what would a better approach be?  The following straw man ensued from the discussion:

• First, in this case, skip the product selection.  There are only two leading commercial products for predictive analytic modeling (SAS, SPSS).  Flip a coin (if you have a three-headed coin look at an open source solution, R or ESS), maybe it’s already on your shelf, blow the dust off.  Better yet, would a standard planning and budgeting package fit (Oracle/Hyperion)?  The next step should give us that answer anyway, no need to rush to buy, vendors are always ready to sell you something (especially at month/quarter end – my, that big a discount!).
• Use the money saved for a strategic look at the questions that will be asked of the model: What are the key performance indicators for the industry?  Are there any internal benchmarks, industry benchmarks or measures?  Will any external data be needed to ensure optimal (correct?) answers to the projected questions?
• Now take this information and do some data analysis (much like dumpster diving).  The key is to find the correct data in a format that is properly governed and updated (no Excel or Access need apply).  The key is accurate sustainability of all data inputs, remember our friend GIGO (I feel 20 years old all over again!).  This should sound very much like a standard Data Quality and Governance Project (boring, but necessary evil to prevent future embarrassment to the guilty).  
• Now that all of the data is dropped into a cozy data mart and supporting extracts are targeted there, set up all production jobs to keep everything fresh.
• This is also a great time to give that contractor or consultant the questions and analysis done earlier, so it will be at hand with a companion sustainable datamart.  Now iterations begin – computation, aggregation, correlation, derivation, deviation, visualization, (Oh My!). The controlled environment holds everybody’s feet to the fire and provides excellent history to tune the model with.
• A reasonable model should result, enjoy!

No approach is perfect, and all have their risks, but this one has a better probability of success than most.

Actionable Healthcare Business Intelligence – 4 Key Elements

In our Business Intelligence (BI) strategy consulting with healthcare clients, we are often asked how to design a metrics program so the data that ultimately populates the dashboards and drill-downs is inherently actionable. They ask: How do we design the BI data collection and presentation systems to focus our corrective actions and other interventions most effectively?

In our experience, metrics facilitate action when they exhibit four key characteristics:

1. A clear definition – the meaning of the metric must be clear and unambiguous. In a surgical services context, the definition of a late start for a scheduled procedure must be precise, and agreed upon by everyone concerned. If three minutes past scheduled start is defined as late, there should be no haggling that four minutes is close enough to be considered on-time. When late surgical starts are aggregated up to a service line or an entire system, especially when used comparatively, the metric must represent a homogeneous population with regard to the definition of the metric.
2. Clear attribution & dimensional focus – the attributes that describe or annotate the specific metric must be clearly defined, and must allow for focused response along some dimension that makes sense to the business operation. Most often these will align with one or more of the following:
   • Organizationally-focused – staff and other aggregated resources (e.g. departments, service lines, programs, care setting) are organized and accountable in alignment with the mission of the enterprise or segment thereof. It is clear where the action is required in the organization in order to achieve the desired effect or outcome being measured.
   • Process-focused – specific processes or standard operating procedures (e.g. standard orders or order sets, care plans, clinical pathways, standard or research protocols) are implemented and tracked for performance and compliance. It is clear in which specific process or activities action is required, in order to achieve the desired effect or outcome being measured.
   • Specific Resource-focused – specific resources (e.g. individual staff or teams, facilities, materials, equipment) are monitored for performance and compliance with standards for quality, operations or regulations. It is clear with which types or instances of these specific resources action is required, in order to achieve the desired effect or outcome being measured.

   Other Primary Entity-focused – specific critical entities that exist in the operational context being measured, each described by a potentially diverse set of differentiating attributes. For example, in a clinical context, patients are critical entities. The set of clinical, demographic, diagnostic, prognostic, treatment, outcome or other differentiating characteristics on patients is routinely examined and analyzed for potential patterns, and possible interventions.

3. Timeliness – the metric must be captured and available to responders in sufficient time to allow an appropriate response. Metrics can evolve from being primarily retrospective, to real-time reporting, to predictive, each of which enables and facilitates a different type of action. At a minimum, they must be reported in sufficient time for a meaningful response to occur.
4. Accountability – with any of the above, someone in the organization must be responsible and accountable for appropriate action and assessment. The responsible party(ies) must be ready to analyze the situation and deploy the appropriate resources, to take specific needed actions in response to the position or value of each metric relative to relevant performance standards or expectations.

Other factors such as high confidence in data quality and its source, effective communications to responders, and authority to act are also critical elements. Metrics programs and BI systems with these characteristics have taken a good first step toward enabling the focus and the improvements for which they are ultimately designed.