Entries in data quality (13)


Improve data quality easily

I was recently reading a study on the benefits of high data quality and stumbled upon a statistic stating that the vast majority of data quality issues stem from poor quality customer data. While customer data may be vast and thus hard to manage it's surprisingly simple to clean up and maintain. In fact, I don't think it takes more than 5 or so steps to clean up customer data and once again return to high data quality. 

Many of these tips can be applies pretty generally when cleaning up your data but I'd like to focus them just a little bit on customer data. 

The first step in managing your customer data is to cut out any unnecessary data you may have. When the quantity of data is reduced the quality is much easier to maintain. Focus on the exact type of buyers that your business appeals to the most and cut out those that are not the most efficient buyers.

Now that you've cut out the unnecessary data it's time to clean up what's left. Research has shown that nearly a quarter of customer data is entered wrong in most business' databases resulting in errors and missed opportunities. A thorough cleansing of your database may be needed at this step. Doing it by hand is often an arduous and long process but thankfully, applications exist to help streamline the process. I'm quite partial to the app Cloudingo to clean up the dirty data in your Salesforce database. Even the cleanest of databases will become a mess again eventually so setting up regular maintenance is a crucial step. 

And finally, stop dirty data before it even enters your database. It's much easier to block data instead of having to find and clean it up later. I'd recommend the app DupeCatcher for blocking duplicate data before it ever enters into your Salesforce.

As you can see, only a few easy steps are required to keep your data quality high and the benefits of clean data are undisputable. 


manage salesforce data with Cloudingo

Just a few days ago, the folks from Symphonic Source treated me to a preview of their newest Salesforce app, Cloudingo. As you may have noticed, I'm quite a big fan of Symphonic Source's first application, Dupecatcher. It's an extremely simple yet useful tool to prevent anyone from entering duplicate data into Salesforce and thus keep the data quality high. 

Unlike Dupecatcher which prevents the user from entering duplicate data, Cloudingo finds and merges all kinds of Salesforce duplicate objects already in Salesforce

My first impression of Cloudingo was that it is a very efficient tool. I had it installed and up and running in a short time. Much of this was due to Cloudingo's quickness. It scans your data and finds the duplicates surprisingly quickly, and presents them in a dashboard. I also loved the clever phrases on the loading screens from the developers. 

Cloudingo comes with 8 pre-set filters ready to scan for duplicates in your leads, accounts and contacts (it even suppors person accounts!). These filters scan the important information (first name, last name, email, phone etc.) though they can easily be tweaked to search for more specific data. If these existing filters are not enough, the process of adding more is pretty straight-forward. Cloudingo seems to cater to both those who want to truly customize their Salesforce experience and those less involved in the process. 

I quite like the set up of the whole application. The UI is very clean and friendly. So far I haven't noticed any hidden tricks or difficulties of use. I'm finding Cloudingo not only efficient in its speed but also ease of use. 

The benefits of clean data are numerous and this is a tool that might help you manage yours quite a bit better. 

Cloudingo is a paid application and it is, unfortunately, not yet available in Salesforce's Appexchange but you can sign up for a free trial right now at the Cloudingo website!


So, what's up with "Big Data?"

I throw around a lot of terms on this blog and was thinking that it might be a good idea to define and discuss their relevance more in-depth. Since I talked about one of the most used concept last time (and essentially let Salesforce define it for me), cloud computing, I thought I'd focus on another buzzword today. "Big Data." What is it exactly, what does it refer to and what is its significance?

What is it?

As its name suggests, the term refers to large amounts of data. I've read somewhere before that we create something like 2.5 quintillion bytes of data every day. It's big, certainly. The majority of this data was created in just the last few years thus giving rise to a phrase to describe it. 

What does it mean today?

Well, that's a tough one. The term has encountered many evolutions since being originally defined and it's hard to pin point just one succinct definition. Beyond its fairly literal meaning, the phrase has also come to describe the tools and process a company uses to manage very large amounts of data. This includes terabytes, petabytes or even numbers beyond in some cases. Big data has become important not just due to the large amounts of data customers produce but also as a way to comply with government regulations in certain cases. As digital data gains more importance in legal matters thus arises the need to save all digital data, emails, documents and other forms of electronic communication, in case a firm faces any kind of litigation. 

Why does it matter? 

Well that answer's not so simple. It'll take more than a paragraph to describe why Big Data is important to a business and what can be gained from it. As I was writing this article I was thinking that it might be more fitting to discuss how a business can successfully leverage Big Data in its own blogpost. Now that you have a basic understanding of what Big Data is exactly, we can examine what do with it. 


A Free Tool To Manipulate Data

I only recently learned about the open source tool, Excel Connector.  I’m sure this isn’t groundbreaking news to any of you, but I was excited to learn of this tool and to play around with it.  I think it has potential for use by a lot of my clients who end up managing their own orgs, but need some extra tools to maintain data quality.

The Excel Connector works with both Professional and Enterprise Editions and utilizes the Office toolkit.  It facilitates quite a bit of data manipulation via Excel spreadsheets (knowledge and experience with Excel is helpful).  With the too, which is free, by the way, you can perform imports as well as queries and mass updates on records in Salesforce.  It works across standard and custom objects.

I found this tool hugely helpful in a short project I was working on for a client.  She had tens of thousands of records that needed one bit of information updated, but as she was on Professional Edition without the API, she wasn’t able to use the Data Loader.

The Connector is not officially supported by, but there is actually quite a bit of useful information on the Salesforce website as well as in the user forums. 

You can get the connector here.  I do recommend spending a few minutes reading the documentation before diving in.

Don’t know how I missed this one.


Sep202011 and Social Contacts

Salesforce’s announcements a few weeks ago at Dreamforce of and of Social Contacts (rolling out next month) attest to the importance of the “social” when it comes to customer data and customer interaction.  These two products facilitate enhancing and updating contact information in a Salesforce org. is a crowd sourced contact database based on Jigsaw that Salesforce subscribers can use to update and correct their contact records.  Social Contacts adds in connections to a contact’s social media accounts on Facebook, Twitter, and LinkedIn. 

At first glance I think these developments are exciting.  We’ve heard over and over again about the rise of Social CRM, whereby social media interactions become as important if not more important than basic customer contact and history information.  Being able to engage our customer where they are, in the social space, can/will make all the difference in business in the 21st century. 

But there are also some things that I think need to be sorted out when/before incorporating our customers’ social presence into their records in our Salesforce orgs.

First and foremost, do we need in insure that the social media IDs that we tie to Salesforce are the business presences of our customer as opposed to their personal presences?  Of course, if accounts are open and public, we are likely not breaking any privacy standards, but still.  Is it relevant to have access to the photos of a prospects weekend fun and then bring them up in a business conversation?  Further, I’m not sure we’re fully prepared to use the information that’s available via social media outlets.  It’s one thing to strike up a modestly personal conversation about non-business matters, it’s something completely different to track and analyze data as a means to improve and accelerate business.

With these new sources of information comes the increasing importance of data governance.  Organizations will need to review and enhance their policies on data usage and data quality maintenance.

I am exciting to begin using these new features as Salesforce rolls out its latest release.  At the same time, I think we need to start thinking through some of these issues and developing some best practices.





Deriving Valuable Information

Cloud computing can lead to a wealth of valuable and critical data.  But this fact can be a mixed blessing.  Along with social networking, organizations can collect and store data from internal communications, internal applications, cloud-based solutions, data sourced from external suppliers, industry data and statistics about verticals and companies, and information from public and government sources.  And the truth is, I probably missed quite a few in this list.

Obviously with the number of sources of information, the sheer volume of the data itself is growing exponentially.  Cheap IT devices like servers, databases, hard drives, and processors, make it easy to save all of this data with a minimal impact on operational costs.  BUT, to gain any real business value from this data, and to remain competitive, you need to be able to analyze and put this information to work as quickly and effectively as possible.

On top of integrating data and information from disparate sources, you need to make sure the data is clean, because without clean data, your data is essentially meaningless, or at best, misleading.

I don’t have the perfect solution to this challenge.  I work with my clients to address it on an individual basis because each client has a unique set of systems and needs.  But I wanted to raise the issue here because it’s not going to get any better, and the sooner each of your organizations begins to strategize around the issue of data and information management the better.


A Look at Data

I had a great conversation this morning with an old friend of mine that works for a database company on the East Coast. Among other things, we discussed the growth of the IT industry, cloud computing, and specifically data storage. I'm passionate about expansion and innovation, so when I have the chance to talk to somebody that works in a part of the IT industry I'm not familiar with, I really do my best to pull as much new information away from the conversation as I can.

Anyway, I asked him to dig into the meat of the struggles of current database technology. We talked about how the world is accumulating data at an exponential rate, and because there is no universal system in place for data storage, there is a lot of room for improvement when it comes to organization and data cleanliness. As organizations with large databases continue to grow, it becomes necessary for them to be able to migrate and maintain their data in the most efficient way possible. In fact, the sheer volume of some databases puts a lot of pressure on companies to find cost effective solutions for data storage and cleaning. By neglecting to keeping a watchful eye on their data, companies can run themselves out of business by simultaneously paying inflating prices and poor customer service due to disorganization.

"Essentially," he explained, "our biggest inhibitor is finding a way to efficiently migrate a database while maintaining it's integrity." I know I've written a couple of articles on the importance of keeping Salesforce databases clean, but I just thought it was really interesting that throughout the entire IT industry, everyone is struggling to find a way to keep their data clean. It's not all doom and gloom though. My friend assured me that there are a number of companies out there that have been working on solving this problem for some time now. I'm sure a solution is just over the horizon.


Data Quality: Case in Point

The motivation for this blog post is a case in point example, so I'm going to go ahead and give you the story first and I think you will understand immediately why I'm so frustrated. For the purpose of this exercise, I'm leaving out all names and identifying traits of the people involved. I'm sure that most of you have experienced a similar situation, so feel free to chip in with comments.

I received an email two days ago from the sales director at my company. He went into detail about a conflict that came to his attention early in the week with two sales representatives, who I'm going to call Tom and Jerry. Tom made a phone call to a customer that he had made initial contact with two months ago and had entered into our Salesforce database. He was confused when the customer told him that he had just talked to a rep at our company named Jerry, and had actually decided to go ahead and buy one of our products.

At this point, Tom was both frustrated and irritated. He had embarrassed himself in front of a customer and lost out on his commission. He immediately went to the sales director with his complaint, who immediately emailed me. So I was left with a problem: how had Tom and Jerry both been assigned the same customer by the Salesforce system?

I found my answer fairly quickly. Every weekend, our Salesforce account does a mass import from our internal database containing all of our customer information. At some point, the customer in question had his phone number changed. When the system did it's weekly import, instead of recognizing that the number had changed, it went ahead and created a completely new account under the duplicate information with a different phone number. Tom was assigned 'Original Customer' and Jerry was assigned 'New Customer' and I ended up with a two-salesperson pileup.

I know that I've shared a lot of my thoughts on data quality with you before, but I don't think it can be emphasized enough. Without maintaining strict protocols for data import as well as entry, conflicts like this can arise fairly easily.


Steps Toward Cleaner Databases

One of the most consistent problems I face in my work is getting members of our sales team to fully and accurately fill out the required fields on This lack of attention to detail has left entries into our database in a patchy condition at best. Some accounts are fully filled out, but nothing is a bigger bane on the efficiency of our sales team than a partially completely lead form.

However, by taking a two simple steps as a Salesforce Admin, you can start to really cut down on the amount of incomplete data that is entered into the system.

1. Be sure that you have set up all fields appropriately for the lead form. If you plan on transferring the information from the lead form over to the account page, be sure that the fields on both pages match up.

2. Next, set up validation rules. By creating efficient validation rules, you can make sure that the user enters in the required data before they are allowed to save the record or change the status of the lead.

Important things to consider: When you are creating the validation rules for the lead forms, think carefully about which fields you want to be required. For example, if the salesperson makes initial contact over the phone, it makes sense to require that the telephone number field is filled in. You have to be careful though, because validation rules prevent the user from saving the information without all of the required fields filled in. What information is it likely that the sales rep can acquire over the phone? What is unlikely?

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