By Ruben Overkemping, CRM Solution Manager, Avanade
In the first article of
this series, we introduced the phrase "data
is the new oil". While the analogies may seem obvious in some ways, there
is also a big difference: oil is something we are running out of, while the
amount of stored data continues to explode. So while oil companies are
endeavoring on the greatest adventures in the most inhospitable places to find
more of this precious resource, the challenge in the world of data seems to be
how to control the flow.
We are drowning in an
ocean of data, looking for ways to escape the full force of the currents while still
harnessing the right insights. In this article, we will focus on how we can
control the flow of data that is coming out of the (inhospitable) world of data
centers by using an actionable insights platform built on Microsoft technology.
The components that make
up this Microsoft platform range across multiple products: Office, SharePoint,
SQL Server, Azure, and Dynamics CRM. To explain how these products work
together, this article will explore the role these products play in the
platform and dive deeper into the products that help a company to get the
know its customer.
Getting to know the customer is the first step in a continuous
process to make actionable insights work for your organization and will be the
focus area for this article. Other steps we see in this process of actionable
insights are reach the customer and deliver the experience. Upcoming articles will describe today's role
and vision of the platform related to reaching the customer and
delivering the experience.
By exploring the theme of
knowing your customer, we are entering the world of business intelligence,
including issues related to data quality and enriching the customer profile.
Knowing your customer in today's world means refining the
organization's data and algorithms to build a high quality customer profile.
The history of the analytic algorithms used today to build these customer
profiles is very interesting. You could say it started in eighteenth century
when the parish priest Bayes thought out his naïve Bayes theorem.
The "naïve" part of the theorem name isn't referring to the mindset of its
inventor but it might as well have as we will see.
Bayes devised a formula that is used to calculate probabilities
which are not only based on the aspects that are known, but even more on the
unknowns (inverse probability). In his own time there was no actual use for
this theorem as there was no way to calculate outcomes for this algorithm
(because of the lack of computing power) and the use cases for using this
inverse probability also were non-existing.
Let's now move from the world of a new theorem that had no
customers (as we use the term today) nor related use cases, and into a world
where Bayes' theorem became like the "discovery of fire", in the
world of marketing in the nineteen eighties and nineties. Bayes and other
theories behind algorithms such as time regression, sequence clustering, etc.
became part of the domain of specialized marketing departments and data
specialists and it usually remains there still. But the world is changing and
today these specialized domains are becoming more and more the domain of
empowered power users in the marketing and customer service departments.
Technology advances and experience with data mining algorithms
provide us today with really empowered self-service analytics and the Microsoft
platform proves this today. Special role within this world is the Microsoft SQL
stack which provides affordable, empowered, high-performance self-service
Most people think of data mining as drilling down into the data to
try to understand the data based on what is available. But data-mining is
providing more thanks to the Bayes theorem as it also provides insights on what
is not in the data itself but can be "predicted" from that data.
Microsoft Excel can become an interface for reaching the
self-service aspects of enterprise analytics, powered by server-technologies
like SharePoint Excel-services, SQL Server Analytics Service, and Microsoft
HDInsight (Hadoop Big Data in the cloud). This empowerment of end users does
not mean that you take a step back in enterprise manageability. Instead, you
take a leap towards consumerization of IT in the field of analytics without losing
the managerial grip.
To get an idea on relevant business questions that could drive the
use of analytics in the world of CRM, consider this non-exhaustive list:
Most of these questions can be answered today by non-technical users doing self-service
analysis through free-form reporting that allows them to integrate data from
disparate sources and drill-down and understand the root cause of data
anomalies. For other questions, it is
still important to look at a broader spectrum of analysis possibilities and
also to use the data integration possibilities to actually enrich the customer
record in the CRM system.
For questions that can be answered by non-technical users performing
their own reporting and analysis certain qualifications of the users and use
cases will include:
We will see more on the use of analytics as part of the upcoming
article around Reaching your customer where
we will show how creating micro-clusters with predictive analytics coming out
of the Microsoft platform and enhancing the CRM campaign-records.
Management: Enriching CRM
The first part of this getting
to know your customer article shows how you can use analytics to answer
business questions around your customers. The other challenge of knowing your
customers is the verification that your customer record actually represents
your customer. One anecdote from my own experience - somehow an online retailer
thinks I am interested in offers for skirts because my girlfriend once used my
account (and credit card) to order herself a skirt. They could have deduced
with all of my other (of course very masculine) gadget orders that the skirt
should have somehow been an anomaly to my customer record.
Microsoft provides the tools in its platform to deduce the quality
of your customer (lead, prospect) data attributes and a way to increase the
data quality around these. Data quality can be managed by using the broader
spectrum of Enterprise Information Management processes to help produce
accurate and trustworthy data. The goal is to deliver credible, consistent data
to the right users with end-to-end data integration, cleansing, and management.
Within the Microsoft SQL stack: Data Quality Services, Master Data Services,
and Integration Services play a role in achieving this.
Quality Services (DQS) provides the functionality to cleanse and de-duplicate
data. This functionality can be used in conjunction with Dynamics CRM to manage
data when it comes into CRM but also to keep the data quality high as part of a
continuous process. From an architectural perspective, think of a job
(a-synchronous worker process) that checks which data records in your system
need a data quality validation and to offer these records to the data quality
services. Use the advanced functionality of the DQS to determine the quality of
the data attributes and correct or enhance the record where needed and update it
in CRM accordingly. As for advanced functionality, think of fuzziness rules
around possible duplicate records and exception highlighting based on inverse
Data Services (MDS) delivers the features to govern the data available in your
systems and to manage the data governance rules to, for example, determine your
Golden Customer Record and hierarchy. In enterprise scenarios, MDS can apply
data security regulations to the different data domains that are applicable.
CRM-integrated MDS scenarios are receiving increasing demand within the
Financial Services Industry where the regulations coming from for example Basel
III and the Dodd Frank Act require the organizations to unconditionally
identify unique customer records and their possible impact on the related
Services is an essential component in many of the cases that are seen
today where CRM has to provide an integrated view of the customer. With the
increasing number of data sources available within and outside of the
organizations the data silo challenges of the past are only going to explode in
the future. Managing the processes of integration through a manageable platform
is essential to prepare for the future integrations coming from and flowing into
internal and external (cloud-based) sources.
Before we go into the other aspects of the
customer-experience-engagement-platform our next article will go into getting to know your customer by using
the Microsoft platform possibilities around Big Data in conjunction with CRM.
recommended read on the history of many common artifacts of our private life is
the book from Bill Bryson, At Home: A Short History of Private Life.