Business Analytics

Top 10 Signs your Company Needs DataCONNECT for Dynamics

By | Business Analytics, ERP

DataCONNECT is a data warehousing solution for Microsoft Dynamics that enables you to create a single source of validated company data you can trust.

Is DataCONNECT a wise investment for your organization? Let’s count down the top 10 signs your company should look into DataCONNECT:

10. You have no idea whether the information in your Microsoft Dynamics system is accurate or not. Maybe…maybe not. Hard to tell. I should go ask Joe…

9. You still have lots of information that’s never been put into Dynamics. Extra information is stored in spreadsheets, specialty databases, old ERP systems, and other line of business systems.

8. You just bought Microsoft Dynamics and don’t have an easy way to populate your new Dynamics system with records from the prior system. DataCONNECT includes data extraction and data import functionality for historical and archival data.

7. You just bought Dynamics and want to keep the new system clean, but still compare data from the prior ERP system. And…you want to be able to drill down into the historical detail.

6. It takes forever to run reports.

5. By the time your reports have finished being created, the information in the report is already obsolete.

4. Your budgets and forecasts are consistently inconsistent and they don’t have the detail you need.

3. You’ve invested in Business Analytics tools, but the underlying data isn’t reliable and can’t easily be shared with the rest of the team.

2. You want to benchmark and manage your KPIs, but creating the right data model is too large of an investment or too complicated.

1. You just want your ERP data to be correct, and easily accessible – preferably without spending hours managing and manipulating data.

DataCONNECT provides one version of the truth. It’s fast, reliable, and we don’t know what you’re waiting for.

Learn more on how DataCONNECT can improve your business.

Download the Fact Sheet

Author: Mark Hatting, Business Analytics Practice Director

Making the Case for Business Analytics

By | Business Analytics

If you’re still running monthly reports and poring over Excel spreadsheets to find the insight hidden in the numbers, you’re working too hard – and worse, you’re missing opportunities to help your company thrive.

Thanks to the nearly unlimited amount of cheap online data storage, the advancement of computer processors, and the brilliance of software programmers, business analytics has been growing exponentially in both demand and usage. Soon, it will be nearly impossible to compete in any industry without a strong business analytics foundation.

Business analytics is critical to achieving digital transformation.

Most businesses collect too much data and use too little of it. If you want to convince your CFO, CEO or other executive that business analytics is worth the effort, bring up the following 4 points.

1. To make better decisions, people need to understand the big picture.

Problems start when departments make decisions without understanding the full implication and impact on the rest of the company.  Data warehouses, like DataCONNECT, connect the data between disparate systems, creating “one version of the truth” and building a strong foundation for analysis.

2. Investing in business analytics increases the return on investment on our technology purchases, our marketing budget, our sales efforts, our production controls – and pretty much everything else we do!

Whatever you do, having accurate information faster is bound to improve your results. Fewer product defects. Fewer customer returns. Better understanding of buyer needs. Accurate sales forecasting. Optimal inventory stock levels. What do you need to know to boost your bottom line? There’s a business analytics tool for that!

3. Business analytics can help our company save money.

Manufacturers are using inexpensive IoT sensors to collect data on industrial equipment. Remote monitoring of machines can keep them from overheating, causing damage and downtime.  Predictive analytics and machine learning can help you understand weaknesses in your systems, and can trigger corrective actions. Banks use predictive analytics to detect credit card fraud and stop the transaction before it goes through. Manufacturers use it to identify defective parts and pull them off the line before shipping.

4. Business analytics can help us attract new customers, and delight the ones we already have, which will boost both top and bottom line revenue.

Marrying company data with big data can help you pinpoint more of your best customers. Big data solutions provide a fast, effective way to query data across multiple systems. In addition to modeling data from ERP/ MRP and CRM systems like Microsoft Dynamics 365, smart companies are streaming real time data from multiple external sources, which can help you respond to customers faster.

What key performance indicators do you track today?  How could business analytics help your company be more competitive and gain more market share? If you’d like to explore your options, reach out to our team to request a free Business Analytics Value Assessment.

Learn More About Our Business Analytics Services

Download the Analytics Brochure

Author: Mark Hatting, Managing Director – Business Analytics

Understanding your Power BI Options

By | Business Analytics

Power BI is cloud-based business analytics service that allows you to build your own BI dashboard using data from a variety of sources. The solution can also be available on-premise through the Power BI Report Server.

Power BI comes in multiple versions:

• Power BI Desktop – Perfect for companies just starting to use dashboards to visualize data, the desktop version is FREE for authoring and publishing! If you’re familiar with using Microsoft Excel, you’ll find Power BI works much the same way – but with better-looking graphs, charts and reports. Power BI Desktop is a great way to proto-type and try out the SaaS.

• Power BI Premium – Power BI Premium is a paid cloud-based service designed for the larger enterprise, corporate user. The Power BI premium has built-in collaboration capabilities and provides dedicated capacity (to ensure high speed service). This option is designed for large scale deployments and large “reader” audiences. Estimate the pricing here.

• Power BI Mobile – Whichever version of Power BI you have, adding Power BI Mobile will keep you connected to the latest office insights. This solution is automatically available with the Cloud version. Simply downloaded the Power BI app on your smartphone and sign in.

• Power BI   for Developers – Pull Power BI data into your apps so you can make better real-time decisions. Companies are combining Power BI with applications to visualize customer data, customer defects, scheduling in real-time. Because Power BI is embedded within the application, users retain the ability to drill down into source data.

• Power BI Report Server – This is the only version of Power BI that is deployed on-premise. This software is a customer version of SSRS. You can deploy on-premise today, and move to the cloud whenever you are ready. If you are already using SQL Server Reporting Services, Power BI Report Server help you find patterns quicker, and an easy-to-explore visual format.

Developers can take Power BI to the next level, creating custom visuals and using an API key to push data into a dataset. Those charts can then be embedded into a software application to be shared internally or externally. Streaming data for IoT (Internet of Things) initiatives is also available, and become popular with Azure and Cortana Analytics. Power BI is putting analytics into the hands of users who need real-time insight.

All Power BI deployments are not created equal. Having a partner who understands Business Analytics best practices can help you maximize your technology investment and insight.

Learn more about Power BI:

Learn More about our Business Analytics Practice

Download the MCA Connect Business Analytics Brochure

Author: Kevin Ballew, Solution Architect

Is the Data Warehouse Dead?

By | Business Analytics

For years, there have been a few technology pundits declaring the Data Warehouse to be a dying technology – to be killed off at any moment by “Big Data” and impressive new business analytics technology.

The truth is that the concept of the data warehouse is alive and well, and will be for as long as companies want to use their own operational data to make business decisions.

Long live the Data Warehouse!

Big Data may be the future, but there’s little evidence that the demand for the Data Warehouse solutions will be slowing down anytime soon.

As with all technology, the tools, technology, architecture and processes used to create a Data Warehouse have changed over the years. If you don’t keep up-to-date, YOUR Data Warehouse may become obsolete, and you will have to find a time to upgrade or replace it. The concept itself is here to stay.

The Influence of Big Data on the Data Warehouse

A Data Warehouse is designed to provide “one source of the truth” for all of your company’s operational information. By pulling data from multiple databases, spreadsheets, and business systems, companies can then analyze and track key performance indicators that factor in all aspects of the business – accounting, production, sales, HR, etc.

One of the first steps in creating a data warehouse is to create a data structure. Historical information is then extracted, transformed and loaded (ETL) into the centralized data store following the relational database structure.  Aging technology and poorly designed extraction processes can create bottlenecks that limit the value of the Data Warehouse, and may be a sign you need to modernize your Business Analytics systems. Innovations from Big Data are influencing improvements to this process.

Big Data pulls in huge volumes of outside information, from customers, social media, bank records, and other data sources. Big Data systems in the Hadoop ecosystem are designed to span multiple machines, and don’t require the data structuring of a data warehouse. The concept of the Data Lake was born from Big Data. This Data Lake of unstructured data can be combined with structured data to provide additional insight. But without the structured data, how can you run a business?

Companies Need Structured Data

Without the Data Warehouse, there is no officially sanctioned, vetted, governed, agreed to, “one version of the truth.” What we see in our Business Analytics practice is that Big Data trends are reinforcing why companies need a Data Warehouse, and are bringing in technology that makes them faster, easier and cheaper to build.

Want more information on Business Analytics?

Download our business analytics brochure to learn more about how MCA Connect can help you define and improve your key performance indicators.

Download our Business Analytics Brochure

Author: Mark Hatting, Managing Director – Business Analytics

Demystifying Business Analytics

By | Business Analytics

Organizations are drowning in data. Business analytics tools provide a way to create order out of chaos, transform confusion into clarity, and turn information into insight. Analytics enables everyone in the organization to become proactive rather than reactive. Conducting analytics projects is what needs to be done to continually improve business activities.

However, there’s still a lot of confusion when it comes to business analytics. People use the term “business analytics” to describe:

  • Reporting tools (like SSRS – SQL Server Reporting Services)
  • Visualization tools (like PowerBI)
  • Analytics suites (like ZAP, Targit, and Halo)
  • Predictive Analytics systems (like Cortana and Azure Machine Learning Studios)
  • Data stores (like SQL Server, Azure & Data Warehouses)

The most common business analytics tool in the world is Microsoft Excel. It slices. It dices. It pivots. People love it for its simplicity, but that’s also it’s limitation. Picking the right solution is essential, and can dramatically impact the quality of your analysis.

Selecting the right business analytics tools

How do you decide? This is an area where a seasoned business analytics consultant can be worth their weight in gold.  On the surface, many business analytics tools appear to have the same functionality. The reality may be quite different.

Whether you work with an expert or not, your selection should be based on these 10 factors:

  1. How quickly you need the information
  2. How accurate the information needs to be
  3. How secure the information needs to be
  4. Where the source data is stored
  5. How much data you’re working with
  6. How frequently you want fresh information
  7. Who will receive the information
  8. How and where information should be presented
  9. How much automation you want
  10. Your time and budget constraints

For example, one of our oil and gas customers stands to lose $2 Million Dollars a day when well production shuts down. Using predictive analytics, they can minimize the chance of this ever occurring.

Other companies may use business analytics to improve forecasting or utilization rates. Speed of information is less critical, but important to the organization overall.

Key piece of advice

When we work with customers, if there is one thing we say repeatedly it is “start small.” Begin with a narrow focus, especially if business analytics is a new endeavor. Gain some quick wins. Prove the value. You can always expand the project scope in a later phase.

Want more information on Business Analytics?

Download our Business Analytics Brochure

Author: Mark Hatting, Managing Director- Business Analytics

Need historical data?

By | Business Analytics

Typically, when companies buy Microsoft Dynamics 365 (or Microsoft Dynamics AX), detailed historical data is not pulled over into the new system.  There are several good reasons for this:

  1. It keeps the new ERP data clean.
  2. A smaller database will run more efficiently.
  3. Mapping and migrating that volume of data will be costly.

However, in some cases, we have clients who really want to have that old ERP system data accessible for forecasting and reporting. Perhaps you want to run year-over-year sales reports, and want to be able to drill into the detail.

In that case, we can build a special connector to our DataCONNECT data warehouse solution that will allow you to analyze your historical data and compare it to the data from Dynamics 365, AX entity and data store and/or other business systems.

Once your historical data has been pulled into DataCONNECT, you can use any of our hundreds of pre-built KPIs to analyze past results or predict future scenarios.

Download the DataCONNECT fact sheet

The trouble with data cubes

By | Business Analytics, Business Transformation, CRM

Data cubes are a vital part of today’s business analytics landscape. In geometry, a cube is 3-dimensional. In technology, data cubes can be multi-dimensional. Data cube technology allows users to slice-and-dice information in various ways to explore the relationship between data points.

While data cube business analytics tools are essential for analysis of business data across the enterprise, deploying them as the only source of modeled data is not sustainable. They represent an immense advantage over older tools, but still have a number of limitations.

  1. Building the data cubes can be a long, slow process.  Cubes can aggregate information from multiple sources. The more sources and the more data, the longer this process will take. Be sure you have dedicated some heavy-duty hardware to handle this job.
  2. Limitation of data fields. A cube is intended to be used for rapid analysis of limited data sets. Cubes are not designed to function with large data sets, and have a limited range of historical data as well as fields (dimensions) that can be loaded. Dividing large data sets across multiple cubes creates duplication and loading problems, resulting in very high maintenance and resource costs.
  3. You’re not looking at real-time data. The data that is pulled into data cubes has already been summarized. Detail can be found in the source system, but users can’t drill into it from the cube. The advantage of this is that analysts have a “snapshot in time” they can use as a benchmark, plus the system will run faster.
  4. Cubes must be optimized for performance. There’s an art and a science to aggregating all this data. If users are complaining that it is taking too long to manipulate data, you’ll need an expert on hand who can optimize the relational database design and find ways to improve cube performance.
  5. Cubes are not designed to be a data repository. Cubes being used as a sole data source for analytics and reporting will ultimately accumulate transactional data and fail to load. Reporting performance from them will slow gradually as the size of the cube grows.

Having worked with business analytics tools throughout my career, we decided to build a data foundation which operates as the data source for all cubes across different business functions. A single data model containing all data across AX modules enables users to build cubes rapidly because the dimensional model used to build a cube is identical to the dimensional model in the data warehouse.

DataCONNECT, mcaConnect’s data warehousing solution for Dynamics 365 and Dynamics AX, is able to be deployed extracting multiple data sources external to AX, such as payroll, billing, logistics, factory management, CRM, Excel, Access and more.

Combining all business-critical data into one single consolidated data model allows for diverse cubes to be built without requiring any transformation. For users not needing a multi-dimensional view of data, the data warehouse is also able to be queried directly from any BI Platform.

The DataCONNECT solution has several distinct advantages over cube technology.

  1. You retain the ability to drill down into the source data
  2. Lowest level of detail is always available
  3. Provides nearly real-time analytics
  4. Hundreds of leading and lagging business indicators are made available to load to cubes
  5. You get answers significantly faster than you would with building data cubes from the ground up
  6. Historical information can also be accessed

Want to learn more? Download the DataCONNECT fact sheet

5 Business Analytics Trends Fueling Manufacturing Growth

By | Business Analytics

As a company that has long specialized in implementing and supporting Microsoft Dynamics for manufacturing companies, it’s exciting to see all the new opportunities coming from our business analytics team. While metrics, business intelligence and insight have always been important to our manufacturing clients, advancements and greater availability of business analytics technology is transforming the manufacturing industry.

The top 5 business analytics trends we’re seeing right now include:

1. Using IoT devices for data collection

Most manufacturers are still trying to figure out how to jump into the IoT opportunity. Collecting data is an easy foray into using this technology. Monitor machines for overheating to keep from damaging equipment. Identify which parts break or need replacement most frequently. Over time, collected data can help you spot trends and reduce downtime.

2. Using predictive analytics

Predictive analytics is the key to having your organization become a “well-oiled machine.” Is that unit likely to have a defect? How much of that inventory will we need? Is that customer likely to buy?  Once solely the domain of hard-core number-crunchers, predictive analytics software programs have become more affordable, easier to use and are quickly becoming a priority for highly competitive manufacturing companies.

3. Augmenting company data with big data

Large enterprises often have such an overwhelming amount of data that it is difficult to obtain meaningful analytics. Big data solutions provide a fast, effective way to query petabytes of data contained across multiple systems.

In addition to modeling data from ERP/ MRP and CRM systems, smart companies are streaming real time data from multiple external sources including websites, customer support center, social media, mobile data, payroll provider, regulatory systems, commodity systems and EDI systems.

Standard Azure data lake technology enables all company data to be staged in real time in one location. Analytics are able to be harvested rapidly with the use of Azure Data Warehouse technology and Azure BI Platforms. Third party platforms such Hadoop, Cloudera and Horton Works enable large companies to query massive data sets more effectively inside Azure.

4. Integrating ERP with cloud-based data stores

The beauty of cloud-based platforms like Azure data lake is the nearly unlimited bandwidth and inter-operability with business systems like Dynamics 365. Records can be stored in the cloud and easily accessed by partners, vendors or even other company divisions, without impacting system performance. The nearly real-time synchronization between systems means that the right decisions get made faster and with less effort.

5. Including the voice of the operator to drive decision making

Daily reports can tell you that the first shift had substantially fewer defects than the second shift, but may not be able you exactly why. By incorporating the voice of the operator into the stream of data points, management may be able to gain insight about the root cause of the issue. Maybe it’s not that the second shift is tired or less experienced, it could be that the first shift took the easy work and left the harder production runs for the second shift to do.

Business intelligence has long been driven by the phrase, “that what gets measured, gets managed.” But with machine learning, predictive analytics and nearly instant business analytics results, a better phrase might be, “Speed wins.” As a manufacturer, you have an unprecedented opportunity to gain a competitive edge through improved analytics and insight.

The Voice of the Operator: Turning data into insight

By | Business Analytics

How ERP systems generate so much data

Those of us who “live and breathe” ERP systems are flooded with information about orders and transactions. There are transactions for shipments, receipts, labor, subcontracting, production orders, kanbans, purchase orders, sales orders, transfers, picking, packing and moving. And we have all kinds of codes and parameters to further define those orders and transactions, such as defect codes, error codes, good quantities, bad quantities and hold codes. Every single order and transaction is stamped with a date and time and who generated it. It’s no surprise then that the data stored in ERP databases is overwhelmingly filled with transaction history, not master data like items, customers or suppliers.

IBM has estimated that of the 2.5 quintillion bytes of data in the world today (that’s 18 zeroes folks), 90% was created in the last 2 years.

With the digital revolution, ERP systems will incorporate even MORE data!

The digital revolution is well underway with smart products, smart processes, smart machines and even smart inventory. Each point can capture every change of status and send it to massive data stores in the cloud. The connected world is upon us. It’s not just hype surrounding the “Internet of Things.”  IBM has estimated that of the 2.5 quintillion bytes of data in the world today (that’s 18 zeroes folks), 90% was created in the last 2 years.

We are awash in data from our formal ERP orders and transactions and from products and production equipment on the shop floor and now even connected to the internet and big data sources.

But…where is the insight?

A popular study conducted by Sidney Yoshida describes the “iceberg of ignorance” that inevitably occurs in an organization. That study found that executives were aware of only 4% of the problems known in the organization. Only 9% are known to managers and 74% are known to team leaders, but 100% of the problems are known to the workers in each process. And most of the time, the workers know WHY the problem exists and often times may have solutions.

But workers are paid to produce and they are singularly focused on production especially when every movement is time-studied and their actual work is constantly being compared to standards or to others on the team. Suggestion systems are great but for real insight we need to be much more focused.

Combining quantitative and qualitative data

What if we could combine the quantitative data from our ERP systems and IoT with qualitative feedback from operators on what the problems were and what solutions might be tried?   Maybe we would actually be able to learn from our data-rich environments.

Here’s what we can do now quantitatively:

  • Sift through massive amounts of “data”
  • Organize that data into what seem to be “facts”
  • Apply “models” to fit the facts
  • Project “trends” using the models
  • Isolate the “exceptions” to the trend

These data points can help us to optimize “what is. ” But we need people’s creativity and problem solving to break through to “what could be. ”

The Voice of the Operator

The leading edge of where we are going is in an idea we call “The Voice of the Operator. ” The goal is to enable continuous improvement by merging quantitative results with qualitative feedback and suggestions from the people who actually work in the process. It’s got the technology bits of cool stuff we don’t really need to know about (Data Lakes, Predictive Analytics, Natural Language Query) but in the end, we want to be able to ask questions of the data including operator feedback and then launch kaizen events for improvement.

Where’s the real problem? How do we fix it?

Intriguing data relationships don’t necessarily mean we understand the root cause. But they do help us focus on a particular problem and then conduct tests that will tell us whether we have the problem corrected or not. Root causes require insight and sometimes that insight can only come from people.

One of my favorite stories took place in a clothing manufacturing plant. The data clearly showed that the second shift operations had a much higher defect rate than the first shift. Everyone’s immediate response is that there was a training problem or poor management. In reality, the root cause was that the first shift guys were cherry-picking the easy jobs and leave the hard stuff for the second shift.

That’s the kind of insight that only comes from people.

Written By:  Phil Coy