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Gathering customer service data doesn’t have to be hard.

January 3, 2019 by Morris Kagan

Gathering customer service data doesn’t have to be hard.

Gathering data is like pulling together ingredients for a very nice dinner. You shop, you carry home your groceries, and you take things out of the pantry to create a memorable dish.

Similarly, data must be researched, collected, and brought together to create a memorable and accurate matrix of the ebb and flow of your company. 

Using data to understand your customers really isn’t as complex as you think, but it does require a thoughtful analysis of where and how you collect meaningful data. By defining which aspects of customer behavior are most significant to your business, you can measure and analyze better ways to engage your customer and ultimately increase your business and sales.

The second part of gathering accurate data is learning how to map it to your financial data. 

Customer data and financial data are two different things, and they are kept in different places. You will likely have to do a lot of cleaning to get good analytics, but you can’t create useful reports unless you have clean, accurate data.  

Once you have a handle on the data available, you can then create a key matrix. To do so, you need to know what you are analyzing and why. Are you comparing apples to apples or apples to oranges? 

Remember, customer data is anything you want it to be — contact information, purchasing and payment history, demographic information, psychographic information and even social media updates.

Resist the temptation to keep track of everything in a spreadsheet. In fact, you shouldn’t even use a spreadsheet! You need a proper database or customer relationship management (CRM) system. 

Next to your company’s financial data, your customer data is the lifeblood of your company. If you lose it, the damage can be irreparable. 

The five best practices for customer data management include:

  1. Choose critical data carefully.
  2. Don’t collect it all at once.
  3. Clean your data.
  4. Validate your data.
  5. Keep your customer data secure.

Filed Under: Uncategorized Tagged With: Customer Data, Customer Service, Customers, Data, Data Analysis

Does Your Data Provide an Accurate Roadmap?

December 27, 2018 by Morris Kagan

Does Your Data Provide an Accurate Roadmap?

No matter what type of business you are in, you have data and it’s important! 

But like many companies, your data may be scattered and located in a lot of different places – which is a big problem and the number one reason you need a data management firm. 

Data that’s undiscovered or not clean, hurts the health of an organization. It prevents you from understanding what is working, what is not, and what you need to do better. 

Getting a grasp on your data is critical to your success and planning for the future. Getting it right makes all the difference. Here are some critical questions to answer:

• Where is all the data that’s relevant to your business? 

• Can you access it quickly when you need or want it? 

• Do you know if it’s accurate, current, clean and complete? 

• Can you easily pull all the data together, no matter what format it’s in or how often it changes?

If you answered “no or not sure” to any of these, you need data management. Data is the foundation to every decision your company will make. You simply can’t function optimally with corrupt or missing data and you certainly can’t use it for analytics. 

Here are five best practices that will prepare your data for analytics. 

  • Simplify access to traditional and emerging data. More data means better predictors – bigger really is better when it comes to data used for business analysts. Access to more data makes it easier to determine which data will best predict an outcome. 
  • Strengthen the data management arsenal with advanced analytic techniques. QuickData provides sophisticated statistical analysis capabilities. Our analysis identifies outliers and missing values that can skew data. 
  • Shape data using flexible manipulation techniques. Preparing data for analytics requires merging, transforming, de-normalizing and sometimes aggregating your source data from multiple tables. We use reshaping transformations like frequency analysis, appending data, partitioning and combining data, and multiple summarization techniques.
  • Share metadata across data management and analytics domains. A common metadata layer lets you consistently repeat your data preparation processes. This promotes collaboration, better productivity and flexibility. 
  • Scrub data to build quality into existing processes. With a data quality platform designed around data management best practices, you can incorporate data cleansing right into your data integration flow. 
  • Analytics is the hottest IT topic – Remember: Underlying analytics is data. Don’t underestimate how important it is to do data right!

Quick Data is a data management company that prepares clients for growth and success. Data management is just one of the ways that we do this. We teach your company to cut down on inefficiencies and how to optimize your revenue with a state-of-the-art automation program that uncovers and solves data related problems. 

Filed Under: Uncategorized Tagged With: Data, Data Analysis, Data Management, Roadmap

Data Management: A Critical Key to Success

December 13, 2018 by Morris Kagan

Data Management: A Critical Key to Success

What is your company’s greatest asset? Your staff, your marketing team, your knowledge process? All of these are extremely important, but your single most important asset is your data. 

Data drives everything. So, the obvious question is: do you understand yours? Is it correct, clean, and organized in a way that others can immediately access it, read it, and use it so your company reaches its maximum potential? 

It’s no longer enough just to manage your data. It must be streamlined to reach its full potential and it must be protected. Data is the driving force behind every successful organization. 

Since data is your most valuable resource, questions you must consider include: 

Is your data easy to access, clean, integrate and store? 

Do you know which types of data are used by everyone in the organization? 

And do you have a system in place for analyzing data as it flows into your organization? 

Brush up on the following concepts before you start your data management journey:

  • Data access refers to your ability to get to and retrieve information wherever it is stored. Certain technologies make this step easy and efficient, which means you spend more time using the data — not just trying to find it. 
  • Data integration defines the steps for combining different types of data. Data integration tools enable you to design and automate the steps that do this work.
  • Data quality is the practice of making sure data is accurate and usable for its intended purpose. This starts from the moment data is accessed and continues through the various integration points with other data. 
  • Data streaming involves analyzing data and then applying logic to the data. It includes recognizing patterns in the data and filtering it for multiple uses as it flows into your organization.
  • Data federation is really virtual data integration. It allows you to look at combined data from multiple sources without the need to move and store the combined view in a new location.
  • Data governance is an ongoing set of rules and decisions for managing your organization’s data to ensure that your data strategy is aligned with your business strategy.
  • Master data management (MDM) defines, unifies, and manages the data that is common and essential to all areas of an organization. Master data is typically managed from a single location or hub. 

The QuickData Platform with the ideal data journey in mind and is structured for fast and accurate results. It’s designed with IT and business collaboration in mind, and it’s ready to transform your analytics programs into big opportunities. 

Filed Under: Uncategorized Tagged With: Data, Data Analysis, Data Management, Data Organization, MDM

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