E-guide BI Analytics Tools Buyer’s Guide Part...

E-guide

BI Analytics Tools Buyer’s Guide Part 1 Your expert guide to BI analytics tools

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Understanding BI analytics tools

and their benefits

Business use cases can

determine the right BI analytics

tool

How to evaluate and select the

right BI analytics tool

E-guide

Understanding BI analytics tools and their benefits

Rick Sherman, Athena IT Solutions

Business intelligence analytics tools can leverage data and convert it

to actionable information that can benefit organizations.

Enterprises are awash in data about their customers, prospects, internal

business processes, suppliers, partners and competitors. Often, they can't

leverage this flood of data and convert it to actionable information for growing

revenue, increasing profitability and efficiently operating the business. Business

intelligence (BI) tools are the technology that enables business people to

transform data into information that will help their business.

Although BI tools have been around for decades and many consider the

industry mature, the BI market is vibrant, constantly innovating and evolving to

meet the ever-expanding needs of businesses of all sizes and industries. Over

the years, many BI tool styles have emerged to match the varied ways that

business people need to analyze data. An understanding of BI tool categories

and styles is needed in order to match your analytical needs with the

appropriate tools.

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Categories of BI analytics tools

BI analytics tools can be grouped into three broad categories that each support

various BI styles and capabilities:

Guided analysis and reporting. This category includes the traditional BI styles

that businesspeople have been using for years to perform recurring analyses of

specific data. Examples include a vice president of sales examining the sales

pipeline, a marketing manager comparing the performance of various marketing

campaigns or a chief financial officer analyzing an enterprise's financial key

performance indicators.

Years ago, this category was limited to predefined, static reports, but now

business users can select, filter, compare, visualize and analyze data using a

variety of tool types. The underlying assumption in this BI tool category is that

the data set and the metrics used will be predefined, but the analysis itself may

vary based on the immediate needs of the information consumer when

performing that analysis.

The IT group or BI team creates most of the BI applications in the guided

analysis and reporting category for end users. However, business analysts also

produce many BI applications using the self-service BI tools discussed in the

next section. Regardless of who creates the BI application, IT will be

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responsible for the underlying data and managing the BI applications used on a

recurring basis.

The BI tool styles in this category include:

Reports

Dashboards and scorecards

Corporate performance management

Spreadsheet integration

BI search

Self-service BI and analysis. This category includes the BI tools business

users use to perform ad hoc analysis of data. This analysis will either be a one-

time-only analysis or the formulation of a recurring analysis that will be shared

with others.

The users of these tools have dual roles: information consumer and analytics

producer, when they share or publish the BI application they create with the

self-service BI tool. Users of these tools typically have the word analyst in their

title (e.g., business, financial or human resources analyst). Management staff

members may also use these tools when they're doing the work of the business

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analyst (or analytical guru) for their manager or peers, even if their titles might

not imply that.

Whereas guided discovery tools operate with a pre-set collection of data and

metrics, the self-service BI tools enable business users to add data and define

new metrics when performing their analysis without requiring IT intervention.

However, there are some considerations to the no IT involvement needed hype

that some BI vendors will pitch. First, IT will manage data source access based

on need, security and privacy rights, so business users performing their

analyses will have to obtain proper privileges to add data sources.

Second, the data sources need to be consumable by the BI tool. Although most

data sources can be easily accessed by BI tools, there may be specific sources

that prohibit access. Third, the data source must be understandable by the

business user, which often requires business people working with IT to get an

explanation of the schema and definitions of the data they need to analyze.

Finally, no matter how easy the BI tool is perceived to be, having IT help train

and support the business in the effective use of these BI tools will improve

business user's productivity and increase the business return on investment of

these tools.

The BI tool styles in this category include:

Ad-hoc reporting and analysis;

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Online analytical processing -- also referred to as OLAP cubes;

Data discovery; and

Data visualization.

Advanced analytics encompasses the tools data scientists use to create

predictive and prescriptive analytical models. This includes predictive analytics,

statistical modeling, data mining and big data analytics software. Here, data

scientists tend to spend a great deal of time doing data ingestion, integration

and cleansing. This category is outside the scope of this article but is mentioned

here in order to provide the entire spectrum of BI tool styles. Here's a look at

other BI tool categories and styles:

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How to buy BI analytics tools

Each of the BI styles discussed here originated as standalone, specialized BI

tools sold by emerging BI vendors. As enterprises recognized their value, the

following occurred:

The emerging BI vendors with new BI styles grew.

The latest BI style was comingled with other BI styles in the products

offered by established BI vendors.

Larger BI vendors acquired the emerging vendors and incorporated their

products into a BI suite offered by the acquiring firm.

A key buying question an enterprise must ask is: Is it better to buy a BI suite

from one BI vendor or to purchase separate products from multiple vendors?

The answer is: It depends. Although other articles in this series will deal with

this question in more depth, there are key concepts to consider. First, you need

to buy what you need, not just acquire the BI product with the most features

because your enterprise may not need all that's offered. The selection process

should be guided by business need and best fit.

Second, an enterprise needs to examine the cost and skills necessary to

develop and manage BI applications, not just purchase or subscription cost.

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Sometimes, BI suites are more cost- and resource-effective than standalone BI

tools; however, there other times in which they're much more complex, resulting

in higher costs, longer development lead times and the need for a greater pool

of skills.

Who buys BI analytics tools?

The investment in and use of BI analytics tools has experienced long-term

growth, regardless of the economic cycle. It has accelerated in recent years as

enterprises are craving data to not just grow and improve, but also to manage

their businesses on a daily basis. Historically, BI has been the domain of large

enterprises due to complexity, costs and the skills required -- but during the past

several years, those factors have changed dramatically, resulting in small and

medium-sized businesses (SMBs) becoming significant BI buyers.

Many enterprises, regardless of size, initially leverage the reporting capabilities

offered by their business application vendors -- such as SAP, Oracle, Microsoft,

Infor and Epicor -- by also using spreadsheets to fill in the gaps, especially

when their focus is on tactical operational reporting. But this approach often

results in data silos, limiting the ability of an enterprise to leverage its BI efforts

to grow revenues and operate more effectively. In addition, this approach

wastes people's time in comparing and reconciling data from these silos -- time

that could be better spent running the business. When the limitations and costs

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of this approach become apparent, then an enterprise is ready for BI technology

that's independent of their operational applications.

In the early days of BI, only industries with the most significant need for data

used BI; today, enterprises in all industries have information-intensive

processes that require BI tools. The scale of the information that needs to be

analyzed will vary by industry and enterprise size, impacting what specific BI

tools should be considered; however, that doesn't impact the particular BI

categories and styles needed.

The BI vendor landscape

It can be overwhelming to examine the BI vendor landscape for the first time, as

there are currently more than 100 vendors. In addition, the BI market has

experienced a significant amount of merger and acquisition activity, so even

people in the industry are sometimes confused as to who sells what.

BI vendors can be split into three groups:

1. Tech titans. The market leaders by sales are IBM, SAP and Oracle, and

they dominate many other technology markets. These companies secured

their top positions by acquiring the market leaders about eight years ago --

Cognos, Business Objects and Hyperion.

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2. Established BI specialists. Several companies, including MicroStrategy,

SAS and Information Builders, have been selling BI tools for many years

and have an extensive number of users.

3. Emerging BI players. These vendors offer the latest wave of BI

innovation, such as data discovery, data visualization and cloud BI.

Tableau, QlikTech, TIBCO, Logi Analytics and Birst are some of the

vendors breaking out of the pack. Although Microsoft might be considered a

tech titan in BI, the company previously wasn't a recognized leader;

however, in the past few years, it has emerged as a significant player,

particularly in the SMB market.

Deploying BI analytics tools

The two deployment considerations are how the business people will access the

BI tools (front end) and where the BI application itself will operate (back end).

Although there are some BI analytics tools that exclusively use desktop client

applications, almost all offer a browser-based client interface that works across

all major Web browsers. BI vendors were slow to implement native-based

mobile interfaces and instead relied on using a browser on a tablet or

smartphone; however, with the expanded use of mobile devices for business,

that's changing.

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Although most implementations deploy BI application servers on-premises in an

enterprise's data center, more applications are being deployed on private clouds

hosted by companies such as Amazon, IBM and Rackspace. When the BI client

interface is browser-based, the decision on whether the BI tool will be deployed

on-premises or in the cloud can be made based on an enterprise's data center

strategy, rather than by limitations in the BI tool. There are emerging BI players

that are exclusively providing cloud-based BI deployments, often in a multi-

tenant software as a service environment with the cloud BI vendor ensuring

security and privacy.

Now that you have a better understanding of the different tool categories, the

vendor landscape and how BI analytics tools are deployed, the next step is to

determine your needs by taking a closer look at some typical use cases for

which these tools are optimized.

Next article

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Business use cases can determine the right BI analytics tool


Before selecting a BI analytics tool, you should create BI use cases

and then match those requirements with BI analytics tool categories

and styles.

Over the years, many business intelligence (BI) tool styles have emerged to

match the varied ways that business people need to analyze data across broad

product categories that include guided analysis and reporting and self-service BI

and analysis. The best practice for selecting a BI analytics tool is to determine

what data your business people are going to analyze and how, so you can

provide them with the right kinds of tools. The decision of what tool to buy

shouldn't be based on which product has the most features, but rather which

product enables the types of analysis your users need and will use.

In this article, we'll take a look at how to determine what BI analytics tool

categories and styles are the best match for your business and technical use

cases. To support that objective, we will identify the key attributes that define

those use cases.

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BI analytics tools: No one-size-fits-all

Selecting the right BI analytics tool for your enterprise could mean the difference

between the software becoming an integral part of your business decision-

making -- or becoming shelfware and the business declaring your BI program a

failure. The fundamental mistake many enterprises make is assuming there's

one BI product that's right for all; as a result, they base their selection on an

extensive features checklist instead of real business needs.

Considerations for creating your BI business use cases

The key data or analytical characteristics that need to be considered in creating

your BI business use cases and then selecting the appropriate BI categories

and styles include:

Data sources. Will your business people use a predefined set of data,

such as from a specific business application or data warehouse, or will they

determine what data they need as they proceed with their analysis?

Performance measures. Are your company's performance measures, also

referred to as key performance indicators or business metrics, already

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defined and accepted or will your business users need to create them on

the fly?

Recurring vs. one-time analysis. Do your business people need a

recurring snapshot of operational performance on a daily, weekly, monthly

or quarterly basis or will the analysis be done just once?

Visual analysis. Do your business users prefer tabular reports, basic

business graphics such as bar, line and pie charts, or more advanced

visualizations such as heat maps, scatter plots and geospatial mapping?

Spreadsheet usage. Are spreadsheets widely used for analysis and are

they likely to continue to be used in the future? Are the spreadsheets being

used to integrate data from various sources or perform sophisticated

business rules or advanced calculations?

Business knowledge of data. Are your business users familiar with the

data, know how different data sources are related to each other and

understand data anomalies such as quality issues and data gaps?

Business analytical skills. How sophisticated are your business users,

analytically? The most sophisticated users will be highly proficient in

various analytical techniques and possibly even in statistics, while those on

the other end of the spectrum will rely on guided analysis limited to filtering

and drill-down functions.

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Selecting the right BI category and style

The following BI use cases are examples that can assist you in selecting the

appropriate BI category and style. Many enterprises will have multiple BI use

cases, so it's important in those situations to match the right BI category and

style to the right business users. Although it may seem that giving every style to

every business person is a good thing, the reality is that it will likely overwhelm

them and prevent them from using the BI tools effectively -- or at all. It's like the

story of Goldilocks: The software shouldn't be too hot or too cold, but just right.

BI use case: Operational snapshots. The business needs a recurring

snapshot of operational performance on a daily, weekly, monthly or quarterly

basis. The performance measures and the data that needs to be reviewed are

well-defined, and the analysis work typically involves period-over-period

performance comparisons or trends. Business people may filter data based on

agreed-upon criteria, but they primarily want to quickly do some analysis and

then get back to their jobs. To make that feasible, they need tabular reports and

easy-to-grasp graphics such as basic bar and sparkline charts. Data

consistency is key, with IT integrating data as necessary in the background.

Recommended BI category and style: Guided analysis/reporting tools

BI use case: Limited exploration. Similar to the first use case, the business

needs a consistent set of data and performance measures available on a

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recurring basis, but here the users want to do limited data exploration

themselves. They still primarily require a mix of basic business graphics and

tabular data, but they also need to be able to drill down into the information for

further analysis. That combination can best be accomplished through BI

dashboards that are a mash-up of several related graphics with underlying

detailed data that users can access, filter and analyze. Dashboards have

traditionally been created by IT, but an increasing number of them are now

being built by business analysts using data discovery tools (more on this later).

Recommended BI category and style: Guided analysis/dashboards

BI use case: Packaged applications. Corporate performance management

(CPM) applications are built to support either specific industries, such as

healthcare, or business functions such as finance. The most prevalent

applications built into CPM are forecasting, planning and budgeting. CPM

applications are often linked to specific enterprise operations systems. Although

this is a niche market, if the application matches your enterprise's needs, then

there are significant advantages to buying packaged software that incorporates

industry best practices into its data and analytical processes. However, the

trade-off may be a lack of flexibility matching your enterprise's unique needs.

Recommended BI category and style: Guided analysis/corporate performance

management

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BI use case: Spreadsheet integration. Many business people use

spreadsheets to gather data from various sources, integrate the data and then

create reports. The gathering and integration processes are often time-

consuming, involve many manual processes and require integration techniques

that may not be known to most business people. Spreadsheet integration tools

expand beyond simply importing data in the same manner as CSV or text files

by accessing integrated data sources and enabling data blending. In this

scenario, business people can continue to use spreadsheets as a BI analytics

tool but rely on the spreadsheet integration tools to perform the gathering and

integration that was time-consuming and error-prone. Business users often

favor this approach because they feel more productive using spreadsheets

rather than a different BI tool for analyzing data. If they're devoted spreadsheet

users, it will be difficult to get them to shift -- and, quite frankly, they may be able

to do more advanced analysis than various BI analytics tools would offer.

Recommended BI category and style: Guided analysis/spreadsheet integration

BI use case: What data is relevant isn't known prior to analysis. In this use

case, not all the data sources or performance measures are predefined and

most analysis is done just once. The users are business analysts who are self-

sufficient in regards to getting and analyzing data -- they're both data- and

analytics-savvy. In fact, they may have created data shadow systems for their

business peers and are the subject-matter experts for IT when they're

examining data source systems. Data shadow systems are spreadsheets used

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to gather, integrate and analyze data from various sources. They often grow

from a simple spreadsheet used to gather data for a single data source to a

complex application involving hundreds of spreadsheets or worksheets pulling

data and integrating from a myriad of data sources. BI ad hoc query tools were

built for business analysts who need to do intensive data exploration to

determine what data is relevant to their analysis.

Recommended BI category and style: Self-service BI/ad hoc analysis

BI use case: Performance measures aren't defined prior to analysis. In this

case, there's a known collection of data sources, but performance measures

may need to be defined while the analysis is being done. As above, the analysis

work may be one-time in nature and typically requires savvy business analysts.

However, these analysts aren't proficient in using query-based tools -- they're

more comfortable with spreadsheets. The best match for them is online

analytical process (OLAP) or pivot table analysis tools that are just like working

in a spreadsheet.

Recommended BI category and style: Self-service BI/ OLAP or pivot table

analysis

BI use case: Not all relevant data sources or performance measures are

known.

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This is another example in which not all the relevant data sources or

performance measures are known at the beginning of analytics applications, so

business analysts will need to blend in data and define performance measures

during the analysis process. In doing so, they may need to engage in extensive

data exploration work -- but they typically aren't inclined toward using either an

ad hoc query tool or OLAP-oriented software. They prefer an easy-to-use BI

product rather than one with a steep learning curve, which points toward data

discovery tools. Such tools may include data visualization capabilities, but

usually include built-in dashboard capabilities that business analysts can use to

deliver analysis results to business managers on either a one-time or recurring

basis.

Recommended BI category and style: Self-service BI/data discovery

BI use case: Visual-oriented analytics. In this case, the business analysts

need advanced visual analytics capabilities to help them analyze data and

present information to others. Advanced visualizations include heat maps,

scatter plots, geospatial maps, Gantt charts, histograms and bubble charts. This

BI style is typically not sold separately but rather is bundled with other BI styles.

If your business people need the guided analysis BI category, then examine

dashboard products that have advanced visualizations rather than the basic

business graphics. If your business users are looking for the self-service BI

category, then data discovery products are your best choice.

Recommended BI category and style: Advanced data visualization

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More than one BI use case

Initial BI implementations may only need to support one BI use case, but as BI

expands in your enterprise, it's inevitable you will need support for different BI

use cases. Although there are BI analytics tools that only support one BI

category and style, there are BI product suites and data discovery products that

will support various combinations of these BI categories and BI styles. Follow-

up articles will examine how to buy BI analytics tools and identify what specific

BI analytics tool is best for your company.

Next article

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How to evaluate and select the right BI analytics tool


Expert Rick Sherman has advice for organizations trying to settle on

BI analytics tools: Figure out which features are must-haves, versus

nice-to-haves.

Selecting the business intelligence analytics tool that's the best fit for your

enterprise is critical to the success of your BI project. This process includes

gathering and prioritizing BI requirements, as well as determining use cases and

tool categories and styles.

In this article, we will define the key features and functions used to evaluate and

select a BI analytics tool. The criteria can be utilized when it's time to create

your request for proposal (RFP).

BI analytics tool selection and evaluation criteria

Although industry analyst product reviews can be a good source of introductory

research, particularly if you aren't familiar with the overall market, these reviews

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are often oriented toward selecting the product with the most features. Your

organization should instead select the BI analytics tool that's the best fit for its

use cases, meets its budget and can be implemented given its resources and

skills. To simplify the process, you may wish to classify the features and

functions to consider as must-haves, nice-to-haves and will-not-use:

Must-haves. This classification should be unambiguous. In other words, if

the product doesn't have this particular feature, it's eliminated from further

consideration.

Nice-to-haves. Although nice-to-have features aren't required, they're often

the differentiators in selecting a product.

Will-not-use. Many BI analytics products have a laundry list of features

that your company may never use. In that case, don't waste time examining

those aspects of products during the evaluation process.

Caveat: Although a product may have the features that meet your criteria, there

may be special considerations for how those features are obtained. For

example, in order for a BI analytics tool to provide these features, are any of the

following required?

1. Custom coding.

2. The purchase of an add-on product from a third party.

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3. A specific product edition, such as an enterprise versus a basic edition.

These conditions all mean additional time and expense. To ensure an objective

evaluation and avoid surprises if this product is selected, you need to determine

how to identify and factor the additional time and cost into product comparisons.

Overall BI features: The must-haves

The following are often must-have features for organizations:

Data sources. Access to various databases and file types such as comma-

separated values file, text, Excel and XML are basic staples of all BI products.

Increasingly, BI analytics tools are providing access to specific applications

such as Salesforce or NoSQL databases. Your specific needs will determine if

these features are must-haves.

Data filters and drill-down. The product should enable the contents in a

tabular report or visualization to be filtered by data values. Filtering is provided

by features such as pull-down lists, search filters and slicers. The product

should also allow the user to drill-down from summarized to more detailed data

and then drill up (i.e., go back to where they started); this is essential in both

tabular reports and visualizations.

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Web-based client user interface. The product's client user interface for the BI

consumer-role should be Web-based. This has become an industry best

practice, as it's more cost- and resource-effective for administration, support

and deployment than desktop-based interfaces. It's a nice-to-have feature if the

BI application creator and administrator roles are also Web-based versus a

desktop-based client application.

Independent and interconnected mash-ups. When the BI style enables

multiple visualizations, including tabular reports, to be displayed on a single

screen, the software should allow for these visualizations to be either

independent of each other or interconnected. If they're interconnected, data

filters and selection affect all visualizations; for example, if a particular attribute

is selected, all visualizations share that attribute.

Visualizations. The BI analytics tool must provide bar, line, pie, area and radar

chart types, as well as the ability to mix and match various combinations.

Security. All BI products must require both user and user role-based security,

designating who can create, modify, publish, use and administer the BI

applications. You may require the BI product to integrate with operating system

or other pre-existing security applications. BI security often involves using the

product's security along with a combination of mechanisms from operating

systems, networks, databases and the source system.

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Microsoft Office Data Exchange. The product must be able to import and

export data with Microsoft Office products, especially Microsoft Excel.

Print and export. The product must allow for print visualizations and tabular

reports to be exported to PDF or other graphics. Tabular reports need to be

exportable to text files at a minimum and, preferably, to spreadsheets.

Must-have features specific to self-service BI use cases

There are several must-have features that are specific to self-service BI use

cases. These are unique because they provide more data management

functionality for the business person creating an analytical application than for

an information consumer who is relying on pre-built BI applications with pre-built

integrated data. These features include:

Select data for analysis. The BI analytics tool must enable the user to select

the data used in analysis and present it as a pivot table-style interface where

dimension attributes are placed in rows and columns, measures are selected

and filters are applied.

Data blending. The product must permit the user to blend data from various

data sources. This includes accessing the data and mapping or creating

relationships with data from multiple sources.

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Create measures. The product must enable the user to create and save

measures or calculations for use in analysis. These are also referred to as

performance measures or key performance indicators.

Create hierarchies. The product must allow the user to create dimensional

hierarchies, such as by geography or product, to group and summarize data.

This establishes the drill-down paths.

Save queries and analysis. The product should enable the BI user to save the

data filters, selections and drill-down paths used in an analysis so they can be

reused.

Overall BI features: The nice-to-haves

These features often are the criteria that become the differentiators in selecting

BI products:

Create and publish by business users. The product enables the user to save

and share his or her analysis with other BI consumers.

Context-based filters. Filters will list only choices that have values given the

current selection of facts and dimensions.

Context-based visualizations. Only visualizations or chart types that are

relevant to the data selected will be listed as options.

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Advanced visualizations. More advanced visualizations include heat maps,

scatterplots, bubble charts, histograms, geospatial mapping and combinations

of each of these, such as bubbles on a map. The best mapping capabilities will

leverage city, state and country attributes in your data, rather than force the

inclusion of longitude and latitude.

Collaboration and social interaction: The BI analytics tool enables a business

community that can share and discuss their analysis. This would include

annotating analysis to share observations and social media enabling discussion

threads or chats.

Storyboarding: Business analysis often involves a process or workflow of

analyzing different data from different perspectives. Storyboarding enables a

series of reports or visualizations to be tied together in a workflow that can be

shared.

Microsoft Office real-time integration. Beyond simple import and export, the

product should provide real-time integration with Microsoft Office products,

which enables business people to embed analytics from the BI analytics tool

into a PowerPoint or Excel presentation, for example, and refresh it

automatically as the data is updated.

Mobile version. The BI analytics tool should be able to differentiate between

viewing BI applications on a Web browser on a mobile device versus a mobile

BI application.

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In-memory analytics. The product should pull data into an in-memory or locally

cached data store. In-memory columnar is an increasingly popular feature

enabling very fast analytics once the data is loaded.

Offline updates. The BI analytics tool, when it stores its own copy of the source

data in an OLAP cube or in-memory columnar data store, should allow users to

schedule automatic data updates.

Performance monitoring. BI products that monitor report and data usage

enable a BI group to improve analytical performance for the business,

eliminating bottlenecks and assessing infrastructure needs.

BI platform administration. Although all BI tools should provide code and

version management, there are many application development features, such

as team development and user administration, that are useful for larger BI

deployments.

Other considerations

Establishing the scope of your BI project in terms of how many people will use it

and what data will need to accessed is the foundation for creating the selection

criteria. Monetary considerations such as the anticipated budgets for the initial

project, sustaining the BI program and expansion the following year also are key

factors in selection criteria. Although you wouldn't go to an architect and just ask

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to design a house without talking about the size, type of rooms and budget, too

many BI evaluation projects have started without any scoping or budgetary

boundaries, resulting in time wasted in examining BI products that don't fit their

need or budget.

The following are often included in evaluation criteria, but since they're very

subjective, it's important to provide clear definitions of what the comparison will

be based on:

Ease of analytical use. There should be different criteria defined for each type

of user: information consumer, business analyst and IT.

Ease of creating BI applications. There should be different criteria for each

type of analytics creator: business analysts and IT.

Speed of access. Query performance will vary based on the complexity of

queries and amount of data involved. Dashboards with multiple visualizations

will need to get query results from many queries. The best practice is to create

several pre-built query scenarios and compare how each product performs on

these specific examples. The worse practice is to just have evaluators arbitrarily

rate the speed.

Scalability. The best practice is to establish a testing environment to determine

scalability in terms of both the number of concurrent users and data metrics

such as volumes, variety and veracity.

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Platform. Do you prefer on premises versus cloud, open source versus

commercial software, operating systems or other infrastructure options?

Training. There should be separate criteria for BI user versus administration

training. Training may include in-person classes, online classes (live or pre-

recorded) or Web recordings for specific features or processes.

Documentation. There should be separate criteria for BI user online help,

versus technical documentation.

Once you've created your evaluation criteria, it's time to select a shortlist of

product candidates and proceed with your RFP process. To help you create a

candidate pool from which to choose, the final article in this series will examine

the leading BI analytics tools on the market.

About the author

Rick Sherman is the founder of Athena IT Solutions, a consulting firm based in

Maynard, Mass. He has more than 25 years of business intelligence and data

warehousing experience, and has worked on more than 50 data warehouse and

data mart implementations across many industry groups, sourcing data from a

variety of business applications. Sherman's book, “Business Intelligence

Guidebook: From Data Integration to Analytics” was published in 2014. He can

be found blogging at The Data Doghouse, or can be reached at

[email protected].

mailto:[email protected]

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