Six Questions You Should Ask Your Analytics Vendor...Question 1: Do Your Analytics Capabilities Match Our Business Needs? As mentioned in the introduction, understanding the types

Six Questions You

Should Ask Your

Analytics Vendor

SpaceTime Insight White Paper

January 2018

© 2018 Space Time Insight, Inc. Page 2

Contents

Introduction ................................................................................................................................ 3

Question 1: Do Your Analytics Capabilities Match Our Business Needs? .............................. 4

Question 2: What Benefits Will My Organization Realize From Your Analytics? .................... 7

Question 3: Can We Trust Our Data and Your Analysis? ........................................................10

Question 4: Can Your Analytics Handle the Complexity of My Data? ...................................12

Question 5: Can Results be Visualized in Formats Relevant to My Business? ....................15

Question 6: Does Your Solution Operationalize Analytics? ...................................................17

Summary...................................................................................................................................19

About SpaceTime Insight ........................................................................................................19


Introduction

In today’s big data world, analytics play a critical role in delivering actionable insights that empower

personnel to act decisively and confidently in any situation. Many organizations are embracing

analytics, making it a cornerstone capability of their strategies. However not all analytics solutions

are equivalent or appropriate for specific needs. Given that the stakes are so high, making the best

solution and vendor choice is paramount to the success of any analytics initiative.

Batch versus real-time, descriptive versus predictive, small data sets versus large data sets,

analyzing geospatial versus time series or temporal data, self-service and on-demand access

versus offline jobs – these are just a few of the topics that enterprises and vendors alike need to

address. Given all these choices and more, confusion is prevalent in the market because analytics

is a generic term whose meaning differs by vendor, use case and business requirement.

Determining the benefits expected from your analytics prior to evaluating different solutions is a

recommended best practice that ensures the analytics you implement will best fit your needs. This

process starts with establishing a vision and specific goals that can then be mapped to the

capabilities of your ultimate analytics solution. The vendor(s) that you choose to evaluate should

transparently and thoroughly describe and demonstrate how their technology, products, and

services meet your needs, goals and long-term vision. Making the wrong analytics software choice

can not only be costly but also compromise your strategic and operational decision-making.

To aid you in your diligence, this paper identifies six key questions that you should address with

prospective analytics vendors to ensure a successful outcome. Use this guidance to help ensure a

best fit for your company and ongoing success of your analytics program.


Question 1: Do Your Analytics Capabilities Match Our Business Needs?

As mentioned in the introduction, understanding the types

of questions you expect analytics to answer for you is

important to knowing what analytics capabilities you need.

For example, are you just trying to understand what

happened last month or what might happen in the future, or

both? Do you just need to know that something happened,

or also what happened and where, when and why it

occurred?

Analytics ranges from descriptive reporting to highly

sophisticated multivariate mathematical models for prescriptive analytics, as shown in Figure 1.

Advanced analytics, often using machine learning, is useful to optimize outcomes, predict likely

outcomes and prescribe recommendations that drive decisive actions and corresponding

favorable outcomes.

Figure 1: Using Analytics to Determine What Might Happen in the Future Provides

Greater Business Value than Analysis of Events That Have Already Occurred

The types of analytics shown in Figure 1 are discussed below:

Descriptive Analytics - summarizes what occurred. You are able to review metrics and related

data to conduct a post mortem assessment of how well your organization performed over a

period of time. This data can then be used to make operational adjustments going forward.

Validate with prospective

vendors whether their

products provide the answers

and insights into the business

questions and issues that

meet your challenges, needs

and goals.

KNOW BEFORE YOU BUY


Descriptive Analytics is commonly used for periodic reporting, with end-users typically

performing most of the analysis (i. e. determining what the results mean) themselves and the

data is provided primarily as an aid to the decision-making process. The majority of business

intelligence products work this way.

Diagnostic Analytics – identifies why, when and where an event or situation occurred. The

analytics results provide information to enable users to perform trouble-shooting and root-

cause analysis. After assessing the situation, users can initiate appropriate remedies.

Diagnostic Analytics is commonly used in operational environments where time-critical access

to data is important.

Predictive Analytics – identifies a probable outcome, enabling organizations to take proactive

measures to avert a situation or take advantage of an opportunity. Predictions are often based

on a series of probabilities and an understanding of current conditions and historical

performance. Predictive Analytics is used to help businesses understand what is likely to

happen, though users still make the decisions and take the appropriate actions based on that

information.

Prescriptive Analytics – identifies the most optimal actions to take out of a set of possible

outcomes. Unlike the other types of analytics, Prescriptive Analytics can make decisions for

users based on business constraints and priorities. This is important when the volume of data

is too large or complex for users to comprehend, and when decisions must be made in real-

time. Prescriptive Analytics is especially valuable when conditions are uncertain or constantly

changing, as the models can adjust to variable inputs as they are provided.

Analyzing events and situations should inherently include the dimensions of space, time and nodes

in a network. That is, where and when did or might something occur, and what impact did that event

have on other parts of a network. A simple example is trying to understand how a traffic accident

(at a location and time) affects the flow of vehicles on a freeway and other roadways (in a network)

connected to that freeway. These three dimensions are an important part of situational intelligence

as without them, a true understanding of a situation cannot be determined. Without knowing either

when the above-mentioned accident occurred or where it occurred, it would be impossible to

determine its impact on traffic elsewhere.

Below are brief decriptions of how the dimensions of space, time and node just mentioned are

important aspects of delivering valuable and actionable insights to users:


Spatial Analytics - provides a geospatial understanding of where assets, resources and

situations are geographically located, as well as their relative proximity to each other and other

items (e. g. people, buildings, fires, trees, other assets) of interest.

Temporal Analytics - provides insight into when situations occurred or may occur. Temporal

analysis utilizes a timeline of what occurred or might occur to show how situations have or

might evolve. Note that this is different from just looking at aggregated data at a single point in

time (e. g. sales by quarter) – the use of time-series data can be used to identify what happened

last quarter and every time interval since then.

Nodal Analytics - provides insight into the logical and/or physical interdependencies and

symbiotic impact of assets, resources, and situations. Nodal analysis helps users understand

downstream impacts (perhaps on customers) and upstream impacts (perhaps on other assets)

of a failure at a specific location in a network. It also helps users determine how critical certain

parts of an infrastructure are by analyzing the impact if a failure were to occur.

To summarize, analytics can perform extremely valuable tasks. But not all analytics and

approaches are alike. Understanding what business problems you’re trying to solve will help

determine what types of analysis your data needs. That brings us to the second question you

should ask.


Question 2: What Benefits Will My Organization Realize From Your Analytics?

Analytics is a broad field with differing products and

solutions. As such, each vendor promises and delivers

technology that provides different output, results and

benefits. Understanding which benefits you require from an

analytics product enables informed product and vendor

selection. You should of course thoroughly examine vendor

promises and seek explicit proof points for each benefit that

you identify as a critical success factor.

As the diagram in Figure 2 shows, effective data-driven

decision-making with a high degree of confidence is a

fundamental benefit that you should realize.

Figure 2: Decision Effectiveness Matrix

The business benefits you

expect to gain from analytics

may vary. Ask vendors for

examples of how their

analytics have saved other

companies money or delivered

other business value and/or

return on investment.

KNOW BEFORE YOU BUY


Analytics vendors ideally should collaboratively work with you to develop a quantifiable and

defensible business case for investing in analytics. Only you know your organization’s needs, skills

and budget, so the vendor should demonstrate how their features and services fit with those needs.

The types of analytics discussed in Question 1 are listed in Table 1 below along with examples of

how you might use those analytics.

It is worth noting that in some instances, analytics may not yield revealing or extraordinary results;

after all, if there is nothing to find or improve upon, that’s positive, worth knowing and reassuring

too.

Table 1: Applications of Analytics

Here are just a few of the ways in which using analytics can lead to measurable cost savings:

Loss and theft reduction - Loss of valuable products occurs across supply networks, retail

outlets, pipelines, electricity transmission and distribution wires and other distribution

networks. Reclaiming even just one or two percent of sales by reducing theft, spoilage,

technical loss and other types of loss quickly results in cost savings. Analytics can locate

points in a network where losses actually occur and are likely to occur, so that preventative

measures can be taken to realize cost savings.

Field service efficiency - Sending properly qualified and equipped field personnel to a customer

site or the location of a remote asset is costly. If personnel make an unnecessary trip or need

Type of Analytics Applications

Prescriptive

Analytics

Operational optimization: Which new services would be most effective? What markets should we pursue? How should we allocate capital? What is the most efficient schedule for our crew?

Predictive

Analytics

Preparation for outcomes: Which part is likely to fail first? Should we maintain or replace that asset? How much should we budget for asset replacement? How much capacity will we have available?

Diagnostic

Analytics

Performance assessment: Which part failed? Why was that alarm raised? When did that customer call last? Which assets are performing irregularly?

Descriptive

Analytics

Performance review: Did we meet our goals? In which areas do we need to improve? How many new customers do we have? How much did we save with that new initiative?


to make repeat trips due to lack of proper information, scheduling, tools or parts, costs quickly

double or triple. Analytics can ensure that tasks assigned to different functional areas are all

fixed on the first visit, that schedules and routes are efficient, and that postponed work won’t

adversely impact quality, safety or revenue.

Improved capital efficiency - Spending less money, or getting more for money spent, is clearly

in every organization’s interest. Analytics can pinpoint whether to repair, refurbish or replace

strategic assets, which capital projects will have the greatest impact or return, and which

projects can be postponed or cancelled without adverse effects. The larger an organization’s

budget, the more dollars can be saved by a one or two percent increase in capital efficiency.

Risk reduction – Organizations of all types have varied risks that they must manage from

availability of supply to market demand. Analytics can provide value beyond simply identifying

exposure to show how to best deploy funds, assets and staff time to mitigate risk by modeling

probable outcomes and prescribing the best course of action in any situation. Other

organizations, insurance carriers specifically, specialize in managing exposure and risk. In this

latter case, effectively managing risk directly impacts a carrier’s own costs for insurance that

in turn impact quoted premiums. Modeling possible losses is valuable for regulatory

documentation and compliance, along with various safety, restoration and legal costs when

losses occur.


Question 3: Can We Trust Our Data and Your Analysis?

You might be reluctant to embark on an analytics initiative

because the quality of your data is unknown. After all,

good data quality is important for accurate, reliable and

actionable results. Some analytics solutions can help by

recognizing and disregarding anomalous or redundant

data and flag suspect data for cleanup. Analytics that

possesses such capabilities makes it possible to move

forward with an analytics implementation, and in the

process of that implementation, proactively identify data

quality problems that need to be addressed.

Trusting your data and trusting analytics results are

separate but related topics. Your analytics initiative can

positively bridge both, especially if your analytics solution factors data quality into its results. It is

not advisable to delay analytics efforts until an ideal state of data readiness is attained, since

organizations seldom reach that state due to the constantly changing nature of their data.

There are several approaches to developing user trust in analytics results. The first is to choose

use cases where results can easily be validated. Early in your analytics program this method can

help all end-users and stakeholders become comfortable and confident in the use of data-driven

decisions based on analytics results and recommendations. This method can also help to detect

data quality problems to correct the problems and train the analytics to recognize and discard “bad

data.”

In some instances, a score may be used to convey to users the confidence of the analysis. Even

though an analysis may show a positive result, the confidence in that result may be low due to a

small number of data points, old data or incomplete data. In other words, a solution is more

trustworthy and valuable if it includes and conveys a confidence metric along with results and

recommendations based on the quality of the input data. Results and recommendations derived

from poor data quality will have a low confidence score, whereas results and recommendations

derived from good data quality will have a high confidence score.

A successful customer-vendor

partnership is one in which your

vendor understands the

importance of data quality. It is

therefore highly advantageous if

the vendor and their analytics

solution can assist your

business to improve the quality

of your data.

KNOW BEFORE YOU BUY


Each source of data presents its own challenges related to data quality, as does the matter of

streaming data “in motion.” Data at rest can be evaluated by analytics to help identify and correct

data quality problems. Data in motion, such as from devices on the Internet of Things (IoT), poses

additional challenges since analysis (and corresponding decisions) might be needed before data

is stored. In these instances, analytics performs a critical role of cleansing and correcting data that

might be collected out of sequence, reported multiple times, captured inconsistently and so on.

Without this analysis of streaming data, it would be difficult if not impossible to present accurate

information and alarms to users.


Question 4: Can Your Analytics Handle the Complexity of My Data?

Success of any analytics program is of course highly dependent

on access to data, so it is important to understand what data

users require to make decisions. This data usually resides in a

variety of different systems, formats and locations as shown in

Figure 3. This creates data and operational silos that force

users into lengthy and manual data collection processes and

opens your business to errors in correlation and analysis of the

data.

Users often resort to spreadsheets as a way of sharing data,

exporting it from one system and importing it into another. This

problem stems from the fact that the various analytics software

operated in multiple departments or functional areas inherently limit what users are able to access.

Even when enterprise data warehouses, data lakes and similar approaches are used to consolidate

data, these approaches are costly and do not fully resolve all of the inefficiencies and problems

caused by data silos.

Figure 3: Varied Data from Multiple Sources, Stored In Multiple Repositories

Make sure the solutions you

evaluate can accommodate

and analyze the variety of

data important to your

business. Also make sure

the data velocity and

volumes you have and

expect can be supported,

including streaming data.

KNOW BEFORE YOU BUY


It’s often not the case that the data doesn’t exist – it’s just that users can’t get to it. To divorce your

organization from this data-rich, information-poor culture, look for software that has the

adaptability and extensibility to span your existing systems. These include your data sources, your

applications and even your analytics software itself. There’s no need to throw away what you have

as it is possible to leverage the investments you’ve already made in an integrated solution going

forward.

Let’s look at how analytics can help break down these silos and improve the quality of decisions

made across your business.

Job one for any analytics software is the ability to access the data you need, including data that

may not seem necessary. Some analytics software works only with traditional databases and

structured data, while others are adept at also handling operational and streaming data and data

in a wide range of formats. If you need to analyze time-series or spatial data, choose software that

can not only access those, but also integrate them with other types of data in your infrastructure.

External data is often overlooked by analytics products. Data about wind and weather patterns,

currency conversion rates, spot market prices, social media, traffic and demographics, to name a

few, can be an invaluable part of your decision-making process.

Once you’ve identified the data you need, you should also assess when and how frequently you

need the data to make effective decisions. If you are only performing descriptive analysis, then

data may be collected and stored for a period of time before you make use of it. But for real-time

operations and IoT applications, analysis of streaming data becomes critical, and software that is

capable of doing that should be identified. Even if you have been using analytics only periodically,

consider what real-time (or near-real-time) analytics might do to operational efficiency. The

frequency of access and the currency of data are important since basing time-critical decisions on

stale data will inevitably lead to poor operational performance.

Increasing volumes of data challenge the ability of analytics solutions to derive insights in a timely

manner, if at all. Assessing the ability to process large volumes of data must therefore be among

your evaluation criteria. It is important to think about how analytics can help users understand large

volumes of data. It is one thing to aggregate lots of data into monthly reports as this can be

performed offline; it is another challenge entirely to analyze even greater volumes of data in real-

time. Since humans cannot process so much data, look for solutions that can navigate through the


mass of data to identify the nuggets of information that are important, be they anomalies in

behavior or opportunities for performance improvement.

Where and how your data is stored is another important consideration. Most businesses use

multiple different data repositories and frequently a mix of different storage technologies (e.g., SQL

Server, Oracle, MySQL, MongoDB, Hadoop, etc.) Data access challenges are exacerbated when

repositories are in different locations as is often the case with operational silos, geographical silos,

or both. This commonly occurs with global companies that have multiple autonomous divisions,

and/or have grown by acquiring or merging with other companies. You should therefore ascertain

whether the analytics vendor offers interface connections to all the systems you need to access.

You should also evaluate whether the vendor’s professional services team has experience

integrating data from multiple sources and interfacing with multiple IT applications and

technologies, or whether tools are offered for you to do the work yourself.

In summary, look for solutions that:

• Combine analytics results, information, and data from disparate sources including other

applications into a single interface

• Enable you to create your own analytics algorithms and models as well as use algorithms

and models from other sources

• Offer a library of analytics so you don’t have to reinvent the wheel


Question 5: Can Results be Visualized in Formats Relevant to My Business?

Consistently making confident data-driven decisions in a

timely manner requires results and actionable information to

be presented in a visually intuitive manner. Data, analysis

results, and alerts must be displayed clearly and with

contextual relevance. Effective visualization is more than just

the choice to display data in a chart versus a table. The

efficacy of visualizing data and results must also include the

ability to customize what is displayed and how it is displayed.

Colors, shapes, icons, terminology, fonts and other visual

attributes must conform to your business’ standards and

common institutional practices1.

Important insights and situations must be easy to identify at-

a-glance to empower your staff to take immediate and appropriate action, especially for mission-

critical operations and time-sensitive situations. Effective visualizations bring forward insights,

issues, challenges, and opportunities so that end-users can quickly recognize a situation and its

severity along with relevant information about the cause and possible related issues. Combining

results into a big picture view further enhances the ability of end-users to take decisive actions.

Your evaluation of any analytics solution should go beyond the basic question of “does your

solution provide data and results visualizations?” and include the ability to:

• Display information in a web browser. This provides immediate access to data for anyone

at any time and eliminates the need to install and maintain desktop and mobile applications

to perform the same function.

1 Consider a business with different assets in the field. One way to visualize a failed asset would be to simply place a dot

or other marker on a map. Color coding, such as red, would likely be used to indicate a problem status. However, a dot or

marker does not give an at-a-glance indication of the specific asset that failed. An improved visualization would be to use

a color-coded icon (such as an image of the asset) that can be instantly recognized and to provide control over the colors

shown under different conditions.

Effective visualizations

customized for your business

are critical to making

decisions quickly and with

confidence. All information

including alarms and alerts

must be displayed clearly and

with contextual relevance to

clearly convey insights at-a-

glance.

KNOW BEFORE YOU BUY


• Format how data itself is presented. The colors, shapes, iconography, terminology, and

fonts all contribute to how information is perceived by users. These properties of the data

should all be configurable based on data values, operating rules and/or conditions that

might occur.

• Configure the presentation of data in ways that make end users highly productive. Most

users have personal preferences and want to look at data in a format or layout that is

comfortable or familiar to them. Layouts may also vary based on the use cases they are

working on.

• View data in whatever format is most helpful in the decision-making process. Maps are

frequently a good starting point for understanding where assets or issues are located, and

these should be complemented by a wide range of charting types, data in tabular format,

diagrams, documents, alerts, videos and access to third party applications where actions

can be executed. All of these display formats should work in concert, providing the context

users need to make informed decisions.

• Filter data using its attributes as well as by time, by spatial area or by network position. The

ability to lasso regions on a map, for example, gives users the flexibility to hone in on

dynamically-created areas of interest, as opposed to being constrained by pre-defined

regions and time periods. This type of filtering also empowers users to quickly block out the

irrelevant data as they work on problem-solving tasks.

Figure 4: A Visual Interface Should Support the Display of Data and Analytics Results from Different

Systems in Different Display Formats to Create a Single Pane of Glass


Question 6: Does Your Solution Operationalize Analytics?

Democratization of data and the insights it provides enable

your organization to scale and realize benefits across

departments. Operationalizing and democratizing

analytics helps make key information available when it’s

needed most to whomever needs it.

Traditionally, advanced analytics has been performed as

an offline process, with users requesting results and jobs

run as a batch process to produce them. While this style

of analysis is still needed for complex tasks, the pace of

today’s business environment dictates that most answers to operational questions cannot tolerate

delays of hours and days. This approach also limits the utility of the analysis as first, the data is

not that current when it is finally delivered, and second, users will receive all the data processed

whether they want it or not, and then have to sort through it to find what they’re looking for.

End-users should have authenticated role-based permission to interactively analyze and explore

data and the results from analytics. Interactivity means a few things. First, it means that users have

the ability to affect the analysis before it is performed, perhaps by providing input parameters that

qualify what they’re looking for (for example, a specific time range, region, or range of values).

Second, users should be able to see the results in any appropriate format. While results are often

presented in charts, in many cases seeing them on maps or in other visual formats may be more

expedient in providing understanding. Third, users should be able to execute the analysis as

frequently as needed. For example, they might be performing a what-if analysis and adjusting

various parameters to determine their potential impact.

End-users should also be able to share the results they generate with colleagues and stakeholders

to enhance collaboration and drive consistency throughout the company. This obviates the need

for multiple people to perform the same analysis, improving productivity and freeing up IT

personnel and resources for other tasks.

All end-users should have self-

service access to launch

analytics jobs whenever they

need as well as have access to

the types of insights and

results with the level of detail

they need.

KNOW BEFORE YOU BUY


Figure 5: When Analytics is Operationalized, Personnel Make Data-driven

Decisions More Quickly

As described earlier, analysis of IoT and real-time streamed data requires a different approach.

Operationalization in this context implies analysis of the data in-motion as it is streaming and

providing insights to users proactively (as opposed to users clicking a button to perform the

analysis). The ability to push data to a user’s screen can significantly improve their effectiveness

as they will become aware of situations that require attention far sooner than before and will

eliminate time spent on tasks that may no longer be relevant or as important.


Summary

Here is a summary of the key takeaways:

• An understanding of your business requirements should drive the determination of what you need analytics to deliver for your organization

• Ensure analytics solutions are able to deliver on the business benefits and results you are seeking

• Look for analytics vendors that understand data quality and can build trust of analytics results in your user base

• Ensure analytics solutions can interface with data across your organization in terms of its disparities, volume and frequency of delivery

• Only consider analytics solutions that can present the data in intuitive visual formats that simplify the decision-making process and draw attention to important information

• Look for solutions that operationalize analytics allowing users to interact with analytical models, their outputs and the underlying data in different formats

About SpaceTime Insight

SpaceTime Insight enables organizations in asset-intensive industries to generate more value from

their people, processes, and assets. Our award-winning machine learning analytics and Industrial

Internet of Things applications optimize operations in motion, in context and in real time. Teams

at some of the largest organizations in the world, including transportation and energy firms and

some of the world’s largest utilities, use SpaceTime Insight software to power mission-critical

systems. SpaceTime is headquartered in San Mateo, CA with offices in Atlanta, Canada, UK, India,

and Japan. For more information, visit spacetimeinsight.com.

1850 Gateway Dr. , Suite 125 San Mateo, CA 94404 USA

Phone: 650.513.8500 Fax: 650.349.3554

www. spacet imeinsight.com

@spacet imeinsght

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Six Questions You Should Ask Your Analytics Vendor...Question 1: Do Your Analytics Capabilities Match Our Business Needs? As mentioned in the introduction, understanding the types