Tuesday, February 21, 2017

Aligning People and Process to Drive Performance

Thursday, 27 October 2016 00:00

Data Integrity Management

Written by

 

Now that you’ve established your data standards and processes, you need to focus on how to establish continuous improvement and oversight to keep the bad mountain of data from returning. As a reminder from our previous blog posts, ownership and design is the core component of Data Stewardship and involves assigning and communicating data ownership and accountability. Next comes quality standards and communicating the relative business value of data and establishing processes accordingly. The third  cornerstone of data stewardship is quality standards.

Now, let’s dive into today’s topic. We’ll discuss the importance of actively engaging and measuring, ultimately making data stewardship part of the fabric of your organization.

 

Plandocheckadjust

 

A key to proactive management of your data includes building into your current operational processes a Plan, Do, Check, Adjust approach. The prime example is when a data owners finds a pattern of misaligned data, they might be tempted to just correct the data. Using a PDCA approach, however, would lead them to investigate the source of the error immediately instead of putting it off until later.

In an ideal world, you’d rarely have a data quality problem going forward if you make data stewardship a priority. But knowing the reality of today’s dynamic business environment, when quality issues arise, having a data improvement plan is important. If your dataset becomes bad, do you have a process, defined methodology, or improvement plan to get it caught up while you’re fixing the cause of the problem? In the event that the design isn’t executed, identify an improvement plan and incorporate it.

The keys to remember for data integrity are the importance of building data monitoring and improvement into your processes the first time and getting the responsibility for clean data tied to people’s operational roles and job performance (have it written into their job description or performance evaluation).

To wrap up the data stewardship series, there are four key areas of focus that when managed intentionally lead to a solid data stewardship environment. To help you get started we have created a “Data Stewardship Checklist.”

 

Thursday, 08 September 2016 00:00

Data Quality

Written by

 

As discussed in our previous posts, all your sales and operational data does not provide the same level of value to your organization. So then why do you think you should manage all data to the same level of quality? Quality is relative to the value the information provides or supports as measured by its contribution to your business objectives. 

The goal is not perfection – because there is a cost to perfection that will not necessarily lead to increased value. A better goal might be... “imperfect – but with intentional and consistent quality”.

Here are some tips to help you develop and manage a balanced data quality program.

“Value Stream” Your Data

The concept here is that if you can’t map (clearly identify the affect of) how the data contributes to a meaningful and measureable outcome for your organization or department, then it is simply an interesting piece of information and not worthy of process control. Note that this does not necessarily mean that the data should not be maintained at all – but it does beg the question.

Begin by outlining the key measurements that drive your organizational or departmental success. These are the outcomes (think KPIs) that must be met in each of your organizational performance areas such as compliance, financial, operations, customer service or reputation management.

Next, go down a level and identify the measurable outcomes that drive success in each of your key measurements for each organizational performance area. For instance, one such measurement in the financial domain may be total sales revenue generated in a period.

Then map the data that contributes to each of those measurements. Be specific and identify the data element’s contribution level by categorizing it as:

  • Direct – has a direct relationship to the final measurement. For instance, the actual GL revenue generated from a sales transaction. This then ties back to a key measurement of profitability.
  • Supporting – is not directly attributed to the measurement, but supports the narrative for the measurement.  For instance, the level of discount applied to the list price to arrive at the net revenue amount.
  • Leading – a direct measurement to the future of the primary area of focus.  For instance, the estimated net GL revenue on open sales opportunities.

Balance is Key

Once you map each of the organizational performance areas, you should validate them based on the tiers you outlined based on our previous Blog post. Then you can more easily identify each of the organizational performance areas. This helps you understand WHY data is important and is valuable in supporting process and data quality communications with your team. This also informs how you must balance your organizational performance areas and therefore your data management and quality programs.

Lastly be sure to look at the data that you maintain that IS NOT on the list created through this analysis. Ask yourself why you maintain it. If you can’t identify how the data provides current or future value, is it worth having? In a world that leads you to believe that more data is always good – it can be argued that without intention and purpose it is just cost and not value.

Design Quality Goals Based on Value

Now you have the information to define data quality goals that are meaningful to your organization. Using data source and business process analysis techniques, you can build processes that increase the value of the right data while not spending time and money on information that does not lead to results that are important to you. You now can build an intentional and consistent quality management program for your data. This is the subject of our next post – in the meantime, click here to read the rest of this blog series.

Want help getting started? Get the Data Stewardship Checklist

 

Thursday, 14 July 2016 00:00

Data Prioritization

Written by

In our previous Data Stewardship post we discussed the core component of assigning and communicating data ownership and accountability. We also created a “Data Stewardship Checklist” that can provide structure to assist in creating and maintaining the missing link. Click here to get the checklist.

Now that the data ownership has been assigned (see previous blog post), you need to prioritize what will be worked on first – and why. It’s critical to define and communicate the relative business value of data and establish processes accordingly. Let’s dive in…

Define high-level data map in tiers

The first step is to define your high level data map in your critical business processes. Specifically, this should be done in logical sets of data (for example, demographic data, versus relationship data, versus transaction data), not at a data field level. Part of this data map should include setting business impact criteria with each process owner and establishing data tiers with simple “ABC” ranking. A good rule of thumb is to categorize the “ABC” ratings, where “A” is Business Critical, “B” is Business Impactful, and “C” is Nice to Have. After you’ve set individual process owner priorities, consolidate the individual process owner priorities into a single consolidated view to make sure there’s no gaps in ownership and priority. Review the overall list for gaps and overlaps, and get the list validated with the individual owners.

Establish appropriate processes to your prioritized data

A key success factor is aligning the appropriate processes based on the priorities of each level of data. As is taught in best practice inventory management, you hand count “A” level items often. “B” level items may do spot counts, and “C” level items you just replenish when you’re out of them. You typically manage the quality of tier “A” data with exception reporting and dashboards, which presents useable information to you instead of having to dig through multiple data sources. An example might be having a dashboard showing all sales pipeline activities where the expected close date is now past-due more than a week.

Tiers “B” and “C” will require less rigorous and frequent audit components. For instance, tier “B” data might just include routine review of the business impactful data into specific job roles and responsibilities. An example would be someone specifically assigned to look for duplicate information between systems on a monthly basis. Tier “C” might be similar but on a less-frequent basis, or could simply be a reactionary process once incorrect data is identified. Setting appropriate processes, timeframes, and owners based on the relative business impact of the information is critical. Line up appropriate cleansing and audit processes with the business impact of the information so you don’t just have a one size fits all process.

Tie data to your accountability to organization roles

Once the data tiers are set and prioritized, and the appropriate data management processes are established, now you want to tie them to the ownership and accountability in your organization. As described in the first post in this blog series, you’ve already established data ownership, now you need to include data process ownership to specific roles and people. To the extent you can, you should establish these new responsibilities in the Human Resource processes such as job descriptions and performance evaluations.

In the next blog post, we will discuss the specifics of how to build quality into the data management processes as part of overall data stewardship.

Want to see more detail on how to get started? Download the Data Stewardship Checklist, and sign up to get email updates from the Data Stewardship blog series where you’ll receive more information about organizing and maintaining your data assets. We’ll send you subsequent emails for each of the data stewardship areas outlined above.

 

 

DPT is proud to support our West Michigan Veterans through sponsoring an event benefiting Folds of Honor, especially a week before Independence Day, for which so many have sacrificed for us to continue to enjoy. Folds of Honor has a tremendous mission to provide educational support to spouses and children of America’s fallen and wounded soldiers.

This year was West Michigan’s 1st Annual Patriot Golf Tournament at Sunnybrook Country Club. DPT’s Jack Kelly, Amy Flick, Henry Morley, and James Reinhardt participated in the tournament put on by a host of volunteers and organizer Brad Laackman. What a great time had by all, supporting a great organization and cause!

patriotgolf

Jack Kelly probably shot the longest drive of his life – one of the most unusual features of an event based on supporting our military was the ability, under close supervision, to “drive” a golf ball from a rifle. Here’s the mark that was left on the ball after its 360 yard firing:

golfball

DPT is not only happy to support a great patriotic cause, but this is also part of our broader core values of community involvement. We have supported a number of local organizations, ranging from Bethany Christian Services, to Grand Rapids Active Commute Week. If you’d like to learn more about that, please visit our Community Involvement page.

If you’re interested in donating to Folds of Honor, please go to www.foldsofhonor.org

 

Thursday, 23 June 2016 00:00

Data Ownership

Written by

In our previous Data Stewardship post we outlined four key areas of focus that when managed intentionally lead to a solid data stewardship environment.  We also created a “Data Stewardship Checklist” that can provide structure to assist in creating and maintaining the missing link. Click here to get the checklist.

bad data mountain

Today we’ll focus on arguably the simplest of the four key focus areas related to Data Stewardship, which is also the one that most often gets overlooked. In order to be successful in this effort you need to put someone in charge of it and create an understanding of its importance. This involves four components:

  • Establish stewardship priorities and a common understanding of “why”
  • Assign accountability to data owners
  • Provide process resources and tools to support improvement
  • Measure results and plan for improvement

Establish Priorities

There is always inherent value in clarity. A foundational success factor is to clearly define your purpose for data management and quality. Explain why it’s important. This seems elementary, but clarity of purpose should never be implied. 

Create a practical connection between data and your desired outcomes. You can do this by outlining the critical decisions that are made based on data. Create a data map indicating the data flow from data collection to making that critical decision. Use practical examples of data quality variation and show how it leads to lower quality strategic and operational decisions. Show your commitment to data stewardship by confidently including it in your organizational fabric.  Invite debate about the program design and the processes involved. Build data ownership into both your operational processes and improvement projects as a rule.

Assign Accountability

In order to be successful, someone needs to be accountable. This does not imply that Data Stewardship is a one-person effort – quite the contrary. While every person, process and system that collects and manages information in your organization plays a role in data stewardship, the assignment of someone to the overall accountability of a data stewardship program is critical. This ensures focus and attention to the overall effort.  It also allows for a much higher level of consistency in both data accuracy and process efficiency.

As part of defining accountability, be clear about the role each person plays. Define the specific activities to assist people, processes and systems in creating a higher quality data environment. Outline peoples’ role in managing data prioritization, data quality definitions and process improvement. Allow each role to manage data improvement projects. Position each data owner in the organization as a resource to assist people in both data collection and data management roles as well as decision makers to be more effective and confident in the information they use.

Commit Resources

Demonstrate that you are dedicated to data quality by committing resources to support improving processes. Fix your data problem at its source or appropriate process point, not as an audit-based correction. While inspections and audits always play a part in a data stewardship program, focusing on designing quality processes should dramatically reduce the need for audit. Implement automated data analysis tools to assist you in catching abnormalities and incomplete data. Do simple things like designing targeted, exception-only based action lists so your team only has to act on high value data.

Active Leadership

Make data quality and data stewardship a part of your leadership mantra. Talk about it, set expectations, plan improvement and measure results. Stick with your vision that you define for your data priorities and your definition of the “right” level of data quality. Prove its importance by making improvements that move you toward the goal. Show and celebrate how the process improvements are benefiting the organization and your team. Importantly, only make a big deal about data quality in areas that warrant the attention. Make a plan and follow the plan – if is it has been defined as critical, treat it appropriately.

Want to see more detail on how to get started? Download the Data Stewardship Checklist, and set up a meeting to go over your unique Data Stewardship needs.

Sunday, 19 June 2016 00:00

Marketing Automation Strategies & CRM

Written by

 

Marketing Automation

Your sales team has mastered the pipeline and opportunity management portion of your new CRM solution and sales have been on a steady increase for an extended period of time. Until recently. Sales have flat-lined with a very real fear they may actually start to show a downward trend. Worse yet, the marketing and sales teams are at odds with one another as to how to solve the problem. Apparently, in an attempt to keep up with the Sales team, Marketing has been flooding the sales pipeline with, shall we say, less than high quality leads. Marketing is frustrated that the Sales team is ignoring hot leads while the Sales team has all but given up trying to figure out which leads are worth following up on!

What happened?

Let’s take a moment to review the events that may have led to the above scenario.

A number of organizations implement a CRM solution due to the growing pains that often follow increased sales and the subsequent demands it puts on the sales and support teams. At some point in time, without streamlining and scaling the sales process, the stress and strain of the sales volume can bring a team to its knees. An effective CRM solution can bring standardization, automation and increased visibility to the sales process which in turn can relieve many of the pain points and permit the sales team to focus their energies once again on closing sales.

The result? A new pain point; the Sales team needs more qualified leads, and the Marketing team needs a more effective way to nurture and qualify leads for the Sales team. If only there was a “CRM” for Marketing.

Enter Automated Marketing!

What’s the next step to improving the management of your sales prospects and increasing your lead conversion ratio? Streamlining and standardizing your marketing processes should be your next step to increasing not only the number of leads in your sales pipeline, but the efficiency of nurturing and the quality of those leads as well.


Automated marketing techniques have been around since the ‘80’s. The simple task of automatically inserting a name on an envelope or letter was one of the first, of many, mundane tasks a marketing automation tool performed. Today’s automated marketing tools do so much more than personalize communications. Wouldn’t you like to know that your emails are being opened? Or who is visiting your website, how often they visit, what they look at and how often?


Per our DPT mantra, before you focus on the tools you need to develop your Marketing Automation processes and strategy before defining which tool is the best part of your overall CRM strategy. Whatever Marketing Automation tool you use, your processes and strategies need to be integrated into your CRM strategy to enable your Sales and Marketing teams to work more effectively together. 

Common functions include:

  • Outbound email communications
    • Create newsletters and email responses with ease and in coordination with campaigns and utilizing your CRM marketing lists
  • Web Analytics
    • Know who is visiting your website and which content they’re downloading or viewing
  • Web form management
    • Easy-to-Build web forms “Contact Us” and customer surveys
  • Lead nurturing campaign with automated scoring  
    • Communicate with and score a lead based on a prospect’s activities (email opens, website visits, content browsed…etc.)

Much in the same way that your CRM has improved the effectiveness of your Sales team, Marketing Automation strategies and processes can help your Marketing team focus their efforts on nurturing a warm lead into a hot lead that any Sales team would love to follow-up on and close!

Having both the sales pipeline and Marketing Automation portions of your CRM actively working together will align your Sales and Marketing teams towards a common goal. At DPT, we can help you take the next step in enhancing your CRM solution by incorporating Marketing Automation processes and functions. Contact us to learn more.

 

 

Your new information system is in, the staff has been trained and are confident in the process.  The painstaking data cleansing and migration effort is barely a faint memory.  Everyone breathes a huge sigh of relief and is eager to charge forward and not look back.  Now fast forward just a few weeks or months... and you are asking why your data quality challenge already seems like such a huge mountain to climb.

bad data mountain

It may start with one of the following pain points...

  • Why are my dashboards and reports noisy already?
  • Which of these views or dashboards are the most meaningful, anyhow?
  • Wait, is this any better than what I had before?
  • How does this information compare to my historical graphs and charts?
  • I thought my new system was going to provide me better information!
  • Did we make a bad system choice?

If this feels familiar, read on before genuine panic sets in.  This may not be a system problem at all.  It may, however, be the result of lack of discipline and business purpose relative to your data management processes.

Many organizations put new systems or different processes in place in an effort to better manage organizational relationships, processes and information.  The problem is that while we have been adequately trained to be good stewards of the time and financial resources at our disposal, we often forget that data needs the same care and focus.  The unavoidable buzz around “Big Data”, “BI” and analytics-based decision making create an illusion of simplicity.  The fact that today’s systems and database environments can easily and inexpensively store vast amounts of data creates a desire for more information.  This desire needs to be carefully and intentionally managed from end to end.  Make sure you have a data stewardship plan as part of your ongoing system and process management discipline.

In our experience, there are four key areas of focus that when managed intentionally lead to a solid data stewardship environment.  We have created a “Data Stewardship Checklist” that can provide structure to assist in creating and maintaining the missing link. Click here to get the checklist.

As part of our ongoing Data Stewardship blog series, we’ll dive into the major areas of data management that you should focus on. Here’s a preview of what’s to come on our blog:

Ownership and Design

This is the core component of data stewardship. This involves assigning and communicating data ownership and accountability.

Data Prioritization

It is critical to define and communicate the relative business value of data and establish processes accordingly.

Data Quality

Is perfection always the goal for every data element you collect?  While one could argue that you are always better off having higher quality data, you need to measure the cost of perfection.

Actively Engage and Measure

Once you have a handle on all the other topics, you need to make data stewardship part of the fabric of your organization through active engagement and appropriate metrics.

Want to see more detail on how to get started? Download the Data Stewardship Checklist, and sign up to get email updates from the Data Stewardship blog series where you’ll receive more information about organizing and maintaining your data assets. We’ll send you subsequent emails for each of the data stewardship areas outlined above.

 

Tuesday, 17 May 2016 00:00

Dan McGraw Joins DPT Team!

Written by

 

My wife Julie and I are both West Michigan natives with a passion for family and community. Our two sons, Caleb and Hunter, both attend Grandville schools and are involved in extracurricular activities from cycling to robotics. We are firmly rooted in the community and value our family and friendships that we have established over the years.

danmcgraw - family

In business, I have been fortunate enough to experience tremendous growth throughout my career while working for great companies. These companies have ranged from small to large, which has provided me great insight into cultural assimilation, methodologies, practices and ultimately leadership. My roles within these companies have ranged from technical, to project leadership, to my most previous role as Chief Operating Officer.

As with all career decisions, I thoughtfully considered DPT after partnering with the company and developing a strong relationship with the leadership team. Following several months of dialogue, it was evident that the vision of DPT was closely aligned with my values, core competencies and ultimately my professional vision. In having met several DPT clients before coming on board, the client relationship focus and value was evident, adding icing to the proverbial cake.

I am looking forward to coupling my experience with the professional powerhouse of analysts and technical architects DPT has on staff. I believe in today’s technology world, there are significant opportunities for DPT to add tremendous value to the existing portfolio, ranging from business intelligence to field services. Ultimately, it seems natural to add additional data driven applications, analytics and clean data to the Customer Relationship Management (CRM), Business Process Management (BPM) and Project Leadership (PL) practices DPT has today.

When considering the growth of data over the past several years, DPT has the ability to help the market bring even more meaning to this data. It is estimated that 1.7mb of new information will be created every second for every human being on the planet by 2020. It is also estimated that more data has been created in the past two years than in the entire previous history of the human race. These statistics among many similar statistics surrounding data were of interest to me as a business leader and are of equal interest in helping drive clean and meaningful data for our clients. As the saying goes, “knowledge is power” and I am looking forward to getting this knowledge into our client’s hands.

I am excited to bring my leadership ability in business and technology as well as my strong partner and client focus to DPT.

Read more about Dan McGraw in his bio >>
Connect with him on LinkedIn >>

 

 

Monday, 16 May 2016 00:00

What Does DPT Stand For Anyway?

Written by

“What does that stand for?” This is a question we often hear when introducing DPT. While the words behind the letters have changed in our 16 years of business, what DPT has stood for all along has stayed the same: Maintaining a clear focus on business performance as opposed to technology implementation. Other guiding principles driving DPT include teaching our clients to fish, addressing people and process factors first in any business change, and promoting Project Leadership to get real and manage risk.

Let’s dive into that first principle for a minute.

Most organizations do not deploy technology and declare success based on loading software and training a few people – it’s to change their business for the better and improve their performance. Those improvements could include process efficiency, improved decision-making, revenue growth and project effectiveness.

The vast majority of performance improvement is dependent on business change, not technology change. At DPT, we focus on the process, people, data, and strategy being aligned toward business objectives. Putting your time and effort into those factors will drive success more than being good at “cool” technology.

DRIVINGPERFORMANCETOGETHER

Today, we’re proud to say that DPT stands for “Driving Performance Together.” And that’s something that will never change.

Our business results orientation, Project Leadership expertise, and people and process focus are what drive our clients’ high rate of success. If you don’t believe us, listen to our clients. What you’ll hear is a common thread about paying attention to client success as measured in business terms, not technology terms:

Watch our client testimonials:

Capturegrcf

Want to know how we Drive Performance Together? The first step to create an efficient, customer-centric and performance-driven environment in your business is to craft a custom Business Case Blueprint – get in touch with us to get started. There’s no cost – and no obligation.  Take advantage of our experience to key in quickly on the most critical metrics in YOUR business by getting in touch with us.

Page 1 of 5