Why Don’t Data Centers Use Data?

Data analysis doesn’t readily fall into the typical data center operator’s job description.   That fact, and the traditional hands-on focus of those operators, isn’t likely to change soon.

But turning a blind eye or ignoring the floodgate of data now available to data centers through IoT technology, sensors and cloud-based analytics is no longer tenable.  While the data impact of IoT has yet to be truly realized, most data centers have already become too complex to be managed manually.

What’s needed is a new role entirely, one with dotted line/cross-functional responsibility to operations, energy, sustainability and planning teams.

Consider this.  The aircraft industry has historically been driven by design, mechanical and engineering teams.  Yet General Electric aircraft engines, as an example, throw off terabytes of data on every single flight.  This massive quantity of data isn’t managed by these traditional teams.  It’s managed by data analysts who continually monitor this information to assess safety and performance, and update the traditional teams who can take any necessary actions.

Like aircraft, data centers are complex systems.  Why aren’t they operated in the same data-driven way given that the data is available today?

Data center operators aren’t trained in data analysis nor can they be expected to take it on.  The new data analyst role requires an understanding and mastery of an entirely different set of tools.  It requires domain-specific knowledge so that incoming information can be intelligently monitored and triaged to determine what constitutes a red flag event, versus something that could be addressed during normal work hours to improve reliability or reduce energy costs.

It’s increasingly clear that managing solely through experience and physical oversight is no longer best practice and will no longer keep pace with the increasing complexity of modern data centers.  Planning or modeling based only on current conditions – or a moment in time –  is also not sufficient.  The rate of change, both planned and unplanned, is too great.  Data, like data centers, is fluid and multidimensional. 

Beyond the undeniable necessity of incorporating data into day-to-day operations to manage operational complexity, data analysis provides significant value-added benefit by revealing cost savings and revenue generating opportunities in energy use, capacity and risk avoidance.  It’s time to build this competency into data center operations.

Does Efficiency Matter?

Currently, it seems that lots of things matter more than energy efficiency. Investments in reliability, capacity expansion and revenue protection all receive higher priority in data centers than any investment focusing on cutting operating expenses through greater efficiency.

So does this mean that efficiency really doesn’t matter? Of course efficiency matters. Lawrence Berkeley National Labs just issued a data center energy report proving just how much efficiency improvements have slowed the data center industry’s energy consumption; saving a projected 620 billion kWh between 2010 and 2020.

The investment priority disconnect occurs when people view efficiency from the too narrow perspective of cutting back.

Efficiency, in fact, has transformational power – when viewed through a different lens.

Productivity is an area ripe for improvements specifically enabled by IoT and automation. Automation’s impact on productivity often gets downplayed by employees who believe automation is the first step toward job reductions. And sure, this happens. Automation will replace some jobs. But if you have experienced and talented people working on tasks that could be automated, your operational productivity is suffering. Those employees can and should be repurposed for work that’s more valuable. And, as most datacenters run with very lean staffing, your employees are already working under enormous pressure to keep operations working perfectly and without downtime. Productivity matters here as well. Making sure your employees are working on the right, highest impact activities generates direct returns in cost, facility reliability and job satisfaction.

Outsourcing is another target. Outsourcing maintenance operations has become common practice. Yet how often are third party services monitored for efficiency? Viewing the before and after performance of a room or a piece of equipment following maintenance is telling. These details, in context with operational data, can identify where you are over-spending on maintenance contracts or where dollars can be allocated elsewhere for higher benefit.

And then there is time. Bain and Company in a 2014 Harvard Business Review article called time “your scarcest resource,” and as such is a logical target for efficiency improvement.  Here’s an example. Quite often data center staff will automatically add cooling equipment to facilities to support new or additional IT load. A quick and deeper look into the right data often reveals that the facilities can handle the additional load immediately and without new equipment. A quick data dive can save months of procurement and deployment time, while simultaneously accelerating your time to the revenue generated by the additional IT load.

Every time employees can stop or reduce time spent on a low value activity, they can achieve results in a different area, faster. Conversely, every time you free up employee time for more creative or innovative endeavors, you have an opportunity to capture competitive advantage. According to a report by KPMG as cited by the Silicon Valley Beat, the tech sector is already focused on this concept, leveraging automation and machine learning for new revenue advantages as well as efficiency improvements.

“Tech CEOs see the benefits of digital labor augmenting workforce capabilities,” said Gary Matuszak, global and U.S. chair of KPMG’s Technology, Media and Telecommunications practice.

“The increased automation and machine learning could enable new ways for tech companies to conduct business so they can add customer value, become more efficient and slash costs.”

Investments in efficiency when viewed through the lens of “cutting back” will continue to receive low priority. However, efficiency projects focusing on productivity or time to revenue will pay off with immediate top line effect. They will uncover ways to simultaneously increase return on capital, improve workforce productivity, and accelerate new sources of revenue. And that’s where you need to put your money.

Breaking Down Communication Barriers with IoT

The Internet of Things holds the unprecedented opportunity to improve the long-standing conflict between facilities, IT and sustainability managers.  Traditionally, these three silos are orthogonal, and don’t share each other’s priorities.

Data generated from more granular sensing in data centers reveals information that has traditionally been difficult to access, and not easily shared between groups.  This data can provide both an incentive and a means to work together by establishing a common source for business discussions.  This concept is becoming increasingly important.  As Bill Kleyman said in a Data Center Knowledge article projecting Data Center and Cloud Considerations for 2016: “The days of resources locked in silos are quickly coming to an end.”  We agree.  While Kleyman was referring to architecture convergence in the reference we believe his forecast applies equally forcefully to data.  Multi-group access to more comprehensive data has collaborative power.  IoT contributes to both the generation of such data and the ability to act on it, instantaneously.

Consider the following examples of how IoT operations can accelerate decision-making and collaboration between IT and Facilities.

IT Expansion Deployments

As service shifts to the network edge, or higher traffic is needed for a particular geographic region, IT is usually tasked to identify the most desired sites for these expansions.  In bigger companies, the possible sites can number 50 or more.  IT and Facilities need to quickly determine a short list.

A highly granular view of the actual (versus designed) operating cooling capacity available in each of the considered sites would greatly speed and simplify this selection.  With operating cooling capacity information readily in hand, facilities can easily create a case for the most attractive sites from a cost and time perspective, and/or create a business case for the upgrades necessary to support IT’s expansion deployments.

Data can expose previously hidden or unknowable information.  Capacity planners are provided with the right information for asset deployment in the right places, faster and with less expense.  Everyone gets what they want.

Repurposing capital assets

After airflow is balanced, and redundant or unnecessary cooling is put into standby through automated control, IT and facilities can view the real-time amount of cooling actually available in a particular area.  It becomes easy to identify rooms that have way more cooling than needed.  The surplus cooling units can be moved to a different part of the facility, or to a different site as needed.

IoT powered by smart software can thus expose inefficient capital asset allocation.  Rather than spending money on new capital assets, existing capital can be moved from one place to another.  This has huge and nearly instant financial benefits.  It also establishes a method of cooperation between the facilities team that is maintaining the cooling system and the IT team that needs to deploy additional IT assets and that is tasked with paying for additional cooling.

In both situations, data produced by IoT becomes the arbiter and the language on which the business cases can be focused.

Data essentially becomes the “neutral party.”

All stakeholders can benefit from IoT-produced data to make rational and mutually understood decisions.  As more IoT-based data becomes available, stakeholders who use it to augment their intuition will find that data’s collaborative power is profitable as well as insightful.

IOT: A Unifying Force for the Data Center

A recent McKinsey & Company Global Institute report states that that factories, including industrial facilities and data centers, will receive the lion’s share of value enabled by IoT.  That’s up to $3.7 trillion dollars of incremental value over the next ten years.   Within that focus, McKinsey states that the areas of greatest potential are optimization and predictive maintenance – things that every data center facility manager addresses on a daily basis. The report also states that Industrial IoT (combining the strength of both industry and the Internet) will accelerate global GDP per capita to a pace never seen before during the industrial and Internet revolutions.

The McKinsey study described key enablers required for the success of Industrial IoT as: software and hardware technology, interoperability, security and privacy, business organization and cultural support.  Translated into the requirements for a data center, these are: low power & inexpensive sensors, mesh connectivity, smart software to analyze and act on the data (analytics), standardization and APIs across technology stacks, interoperability across vendors, and ways to share data that retain security and privacy.

Many of these enabling factors are readily available today.  Data centers must have telemetry and communications.  If you don’t have it, you can add it in the form of mesh network sensors.  Newer data centers and equipment will have this telemetry embedded.  The data center industry already has standards that can be used to share data.  Smart software capable of aggregating, analyzing and acting on this data is also available. Security isn’t as well evolved, or understood.  As more data becomes available through the Internet of Things, the network must be secure, private and locked down.

Transitions always involve change, and sometimes challenge the tried and true ways of doing things.  In the case of industrial IoT, I really think that change is good.  Telemetry and analytics reveal previously hidden information and patterns that will help facility professionals develop even more efficient processes.  Alternately, it may help these same professionals prove to their executive management that existing processes are working very well.  The point is that to date, no one has known for sure, because the data just hasn’t been available.

The emergence of IoT in the data center is inevitable, and facility managers who embrace this change and use it to their operational advantage can turn their attention to more strategic projects.

My next blog will address how telemetry and IoT can break down the traditional conflicts between facilities, IT and sustainability managers.

Stay tuned.

Analytics in Action for Data Center Cooling

When a data center is first designed, everything is tightly controlled. Rack densities are all the same. The layout is precisely planned and very consistent. Power and space constraints are well-understood. The cooling system is modeled – sometimes even with CFD – and all of the cooling units operate at the same level.

But the original design is often a short-lived utopia. The realty of most data centers becomes much more complex as business needs and IT requirements change and equipment moves in and out.

As soon as physical infrastructure changes, cooling capacity and redundancy are affected.  Given the complexity of design versus operational reality, many organizations have not had the tools to understand what has changed or degraded, so cannot make informed decisions about their cooling infrastructure. Traditional DCIM products often focus on space, network and power.  They don’t provide detailed, measured data on the cooling system.  So, decisions about cooling are made without visibility into actual conditions.

Analytics can help. Contrary to prevailing views, analytics don’t necessarily take a lot of know-how or data analysis skills to be extremely helpful in day-to-day operations management. Analytics can be simple and actionable. Consider the following examples of how a daily morning glance at thermal analytics helped these data center managers quickly identify and resolve some otherwise tricky thermal issues.

In our first example, the manager of a legacy, urban colo data center with DX CRAC units was asked to determine the right place for some new IT equipment. There were several areas with space and power available, but determining which of these areas had sufficient cooling was more challenging. The manager used a cooling influence map to identify racks cooled by multiple CRACs. He then referenced a cooling capacity report to confirm that more than one of these CRACs had capacity to spare. By using these visual analytics, the manager was able to place the IT equipment in an area with sufficient, and redundant, cooling.

In a second facility, a mobile switching center for a major telco, the manager noticed a hot spot on the thermal map and sent a technician to investigate the location. The technician saw that some of the cooling coils had low delta T even though the valves were open, which implied a problem with the hydronics. Upon physical investigation of the area, he discovered that this was caused by trapped air in the coil, so he bled it off. The delta T quickly went from 3 to 8.5 – a capacity increase of more than 65 percent – as displayed on the following graph:

 

DeltaT

These examples are deceptively simple. But without analytics, the managers would not have been able to as easily identify the exact location of the problem, the cooling units involved, and have enough information to direct trouble-shooting action within the short time needed to resolve problems in a mission critical facility.

Analytics typically use the information already available in a properly monitored data center. They complement the experienced intuition of data center personnel with at-a-glance data that helps identify potential issues more quickly and bypasses much of the tedious, blood pressure-raising and time-consuming diagnostic activities of hotspot resolution.

Analytics are not the future. Analytics have arrived. Data centers that aren’t taking advantage of them are riskier and more expensive to operate, and place themselves at competitive disadvantage

A Look at 2014

In 2014 we leveraged the significant company, market and customer expansion we achieved in 2013 to focus on strategic partnerships.  Our goal was to significantly increase our global footprint with the considerable resources and vision of these industry leaders.  We have achieved that goal and more.

Together with our long-standing partner NTT Facilities, we continue to add power and agility to complementary data center product lines managed by NTT in pan-Asia deployments.  In partnership with Schneider Electric, we are proud to announce the integration of Vigilent dynamic cooling management technology into the Cooling Optimize module of Schneider Electric’s industry-leading DCIM suite, StruxureWare for Data Centers.

Beyond the technical StruxureWare integration, Vigilent has also worked closely with Schneider Electric to train hundreds of Schneider Electric sales and field operations professionals in preparation for the worldwide roll-out of Cooling Optimize.  Schneider Electric’s faith in us has already proven well-founded as deployments are already underway across multiple continents.  With the reach of Schneider Electric’s global sales and marketing operations, their self-described “Big Green Machine,” and NTT Facilities’ expanding traction in and outside of Japan, we anticipate a banner year.

As an early adopter of machine learning, Vigilent has been recognized as a pioneer of the Internet of Things (IoT) for energy.  Data collected over seven years from hundreds of deployments continually informs and improves Vigilent system performance.  The analytics we have developed provide unprecedented visibility into data center operations and are driving the introduction of new Vigilent capabilities.

Business success aside, our positive impact on the world continues to grow.  In late 2014, we announced that Vigilent systems have reduced energy consumption by more than half a billion kilowatt hours and eliminated more than 351,000 tons of CO2 emissions.  These figures are persistent and grow with each new deployment.

We are proud to see our customers turn pilot projects into multiple deployments as the energy savings and data center operational benefits of the system prove themselves over and over again.  This organic growth is testimony to the consistency of the Vigilent product’s operation in widely varying mission critical environments.

Stay tuned to watch this process repeat itself as we add new Fortune 50 logos to our customer base in 2015.  We applaud the growing sophistication of the data center industry as it struggles with the dual challenges of explosive growth and environmental stewardship and remain thankful for our part in that process.

 

Why Don’t Data Centers Use Data?

Data analysis doesn’t readily fall into the typical data center operator’s job description.   That fact, and the traditional hands-on focus of those operators, isn’t likely to change soon. But turning a blind eye or ignoring the floodgate of data … [Read more]

Does Efficiency Matter?

Currently, it seems that lots of things matter more than energy efficiency. Investments in reliability, capacity expansion and revenue protection all receive higher priority in data centers than any investment focusing on cutting operating expenses … [Read more]

Breaking Down Communication Barriers with IoT

The Internet of Things holds the unprecedented opportunity to improve the long-standing conflict between facilities, IT and sustainability managers.  Traditionally, these three silos are orthogonal, and don’t share each other’s priorities. Data … [Read more]

IOT: A Unifying Force for the Data Center

A recent McKinsey & Company Global Institute report states that that factories, including industrial facilities and data centers, will receive the lion’s share of value enabled by IoT.  That’s up to $3.7 trillion dollars of incremental value over … [Read more]

Analytics in Action for Data Center Cooling

When a data center is first designed, everything is tightly controlled. Rack densities are all the same. The layout is precisely planned and very consistent. Power and space constraints are well-understood. The cooling system is modeled – sometimes … [Read more]

A Look at 2014

In 2014 we leveraged the significant company, market and customer expansion we achieved in 2013 to focus on strategic partnerships.  Our goal was to significantly increase our global footprint with the considerable resources and vision of these … [Read more]