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5 Steps for Higher-Quality Data

Higher-Quality Data, data, data science, data, data scientist, data management, data modeling, data model, smart data

Introduction

Source: Harvard Business Review

There’s one thing that you as a business owner really has to look out for, and that’s poor data quality.  Machine learning (ML) especially can get harmed by using data that’s bad, as higher-quality data is a high demand part of ML.  Bad data can appear in historical data that’s used to train the predictive model, causing new data contamination, therefore giving bad solutions to future decisions that might be made for the business.

In order to train the predictive model correctly, the data’s correct, properly marked, formatted and be the right data.  You can’t make a predictive model if by mistake the data scientist is given the wrong information to sort out.  Presently most data fails to meet standards.  Causes include that the data creators don’t understand what’s expected of the data, measurements are poorly calibrated, processes are to complex and just plain human error.

It can in turn take up to 80% of data scientists time just to clean up the data given to them, even though it isn’t guaranteed that everything’s repaired before putting it into the predictive model.  This can cause further problems as more and more ML technology becomes popular.  The output from one model feeds another and another, all the way down the line, crossing department lines.  So if there’s even a small error it will cascade, causing more and more errors.

5 Steps of Higher Quality Data

  1. Clarify Objectives and Assess if you Have the Right Data to Support the Objectives – If it doesn’t meet goals, find new data or scale back goals, or both.
  2. Build Plenty of Time to Execute Data Quality Fundamentals into the Overall Project Plan – Start 6 months’ out
  3. Maintain Audit Trail Preparing Training Data – Helps to understand biases and limitations in the model. The audit trail helps to sort it out.
  4. Charge Specific Person or Team with Responsibility for Data Quality When Releasing the Data Model – They need to have some strong knowledge of the data, set and enforce standards, and are in charge of finding and getting rid of the root causes of any errors found.
  5. Have Independent, Exact Quality Assurance – The key word here is independent.

Summary

These steps won’t fully guarantee that your data is completely error free.  But it’ll be better than using data that hasn’t gone through these 5 steps.  This in turn makes for use of an extremely powerful tool in ML.  Think of everything that done if the data is of a higher quality, and how much more you can learn about your business, the competition, and your customers.

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5 ways companies are using big data to help their customers (via VentureBeat)

big data, enterprise data, analytics, data analytics, data modeling, data science, data modeling, data model, data, data science, data scientist, data management,

Five ways companies are using big data to treat customers more like individuals — and build better long-term relationships so those customers happily buy more and more

Source: VentureBeat

Review

As we all remember, back in the day you could go to the store and the clerk would know you personally.  They would ask you how you are and how your family is. It was a very personal relationship you would have, therefore creating loyalty between you and the store.  It has been lost for a while when stores started to sell online.  There were no programs to make your shopping experience more personal or enjoyable.  You just went online to search and buy.  Big data helps to build relationships again as it can help companies offer better service to customers if used.   Here are the five ways that big data helps online stores to treat their customers more like people instead of just numbers.

5 Methods to Use Big Data

  1. Prediction – Big data can help analyze past behaviors of customers to build a more personalized experience for them. This in turn creates satisfaction for the person and increases purchases.
  2. Excitement – This is more for wearable technology. FitBit and other companies spew out the data they gather to their clients, which makes the client more interested and excited to see improvements.  This is completed in other industries too, not just the health industry.  There are apps to help track finances too and make people excited to invest more.  Showing the data makes the client happier.  It can show them where they need to work to improve themselves too.  It’s a good tool for the customer to use.
  3. Improvement – Customer service is just as important as effective marketing and product development. Big data can help in all these areas too.  Representatives can answer questions more quickly and effectively when the correct data is in front of them.  This way the customer doesn’t feel like they are being badgered.  The data helps as the customer has so many ways to get a hold of companies now than before.
  4. Identify – Find the difficulties customers are having to improve their experience. It’ll make for happier and more loyal customers.
  5. Reduce – This deals with the health care industry for improving quality of patient care. It helps to cut cost and improve treatments.

Summary

Big data helps companies now to understand their customers better.  This helps agencies give better services and build relationships again, in a more modern way.  Just consider all the possibilities.  I would think about switching over myself if I had a bigger company and could afford it.

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CRM vs ERP: What’s the difference and which do you need? (via CIO)

ERP, CRM, data, data science, data management, data scientist

Both CRM and ERP systems handle contacts, companies, quotes, orders and forecasts… and they may handle line-item configuration, bundles, delivery schedules and invoices. Where does one start and the other stop? Behold this guide for the bewildered.

Source: CIO

Review of Article

What are the differences between CRM and ERP?  Both have similarities and differences at the same time.  CRM’s purpose is sales and support type companies.  In other words, they work directly with the customer, but don’t directly deal with fulfilling orders.  ERP users on the other hand focus on the process and logistics of producing items to sell.  They don’t usually call customers unless it’s to reply to complaints.

In larger companies they use both types of IT systems.  ERP handles the distribution centers, supply chains, currencies and manufacturing plants.  CRM benefits support purposes, sales and marketing, both domestic and international.  Smaller companies don’t need the entire package, they only will use fragments of either CRM or ERP,  such as accounting packages and contact management systems.

What is ERP?

ERP deals with financial data, production and optimization.  This type of system manages transactions, accounts payable and receivable, taxes, cash flow management, and quarterly statements.  It also handles production schedules, procurement, inventory, fulfillment centers and supply chain management.  ERP lastly coordinates production across many manufacturing plants, wants to find ways to maximize profitability, and improve performances of supply chains to name a few things that it does.

What is CRM?

CRM deals with sales force automation.  CRM has to support these business processes:

  • Lead Qualification
  • Forecasting and Pipeline Management
  • Creating Quotes and Construct Orders
  • Account Management
  • Renewals / Repeat Orders

Summary

Personally if my company were big enough I would consider buying and using bits and pieces of both, not integrating the two systems together, but doing something like before mentioned.  It makes sense to me to just get the parts I need to run my business, as much as I can.  The question becomes, which one is what your company needs?  Do you focus more on the customer directly or strategies and logistics behind the scenes?  Or do you think you’ll need bits and pieces of both?

 

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Enterprise Agility, Digital Options and IT

enterprise agility, digital options, measurement, information technology, IT,

Define Enterprise Agility

To be able to operate and be strategic, there are tons of factors to consider.  Hyper-competition, higher demands from customers, advances in tech and changes in regulations have to be considered when upgrading.  There’s something called enterprise agility.  It’s comprised of two things. Being able to sense what is going on and respond to it.  The timelier the response, the more likely the company will succeed in difficult times.  Dynamic capabilities are included here.  One has to consider how to build the new system that will respond to the new environment.  In order to keep up with competition capabilities have to be able to adapt to new changes.

Market orientation deals with market intelligence focusing on new and current customer needs.  The information gathered will be spread across all departments so there can be company-wide responsiveness to changes.  Absorptive capacity deals with how well the company assimilates, transforms and uses the knowledge gathered.  Strategic flexibility is how well the agency manages both economic and political risks to market threats.  It also looks for future opportunities.  The flexibility has to include both reactive and proactive approaches to problems.  Organizations need to be watchful for and respond to competitors’ actions.  They need to look at consumer preferences.  There has to be a focus on economic shifts.  The agency needs to look out for  changes in regulations and advances in technology too.

The Role of IT

The role information technology (IT) plays in this is very important.  Responses are completed through direct and indirect means of digital options.  Through direct means IT can anticipate and sense changes dealing with businesses.  IT systems help as sheer volume of information processed goes further than can be handled otherwise.  Indirect means are more pronounced though.  This is where product development, manufacturing and supply chains add to performance of the organization.

Digital options can be considered indirect too due to them being work processes and knowledge systems.  Knowledge reach is the comprehension and accessibility of codified knowledge available.  If the system is built correctly it can help companies to gather and use the knowledge gained.  Knowledge richness is when IT gives out high-quality information in a timely manner.  IT also reviews real-time pattern recognition and monitors data.  It helps create strategic scenarios assisting strategic decision making.  Process reach is when IT integrates customers, suppliers and partners internally.  The richness of these processes are improved by quicker timeliness of delivery.  They become more accurate and relevant.

Conclusion

Depending on how the new systems are deployed and managed, IT could hurt as well as help businesses.  The older the technology being used means responses could be limited by the range available.  Everything would have to be updated, not just programming.  Systems might restrict the ability to retrieve and interpret data being gathered for analyzing.  Or processes could be incompatible with new systems.  This would mean that they would have to be upgraded.  It’s up to the firm as to how well IT is going to work for them if at all.  Agility is the ability to respond quickly to change that comes up.  The company has to understand the updates in order to create and implement them correctly.  If not, they could fall behind from their competition even more.