Month: March 2017

Tech Terms: Gamification

Gamification means using digital game design techniques in non-game context, such as education, business or social. For example, a business can take techniques used by game designers and apply it to a non-gaming website to improve customer engagement and loyalty.

Taking popular elements of games and applying it to non-game actions is not a new idea. Gamification has been used for many years in the form of traditional hotel loyalty clubs and credit card or retail store reward programs. Gamification is a buzzword today because it is popular with younger, tech-savvy consumers who tend to look for engaging experiences.

Gamification Is Not Games For Business

Gamification is not about creating games for business or creating something new — the techniques are applied to an existing experience or product to help a business improve high value interactions with customers and partners.
One of the most commonly used gamification techniques is rewarding the customer. In a game, the player is rewarded by accomplishing specific tasks. In business context, a reward program is used to encourage customers to accomplish specific tasks that will improve the overall loyalty and interactions with the company or brand.

The Future of Gamification

According to research firm Gartner, the current success of gamification is largely driven by novelty and hype but analysts say gamification is positioned to become a highly significant trend with 70 Percent of organizations having at least one gamified application by 2014.

Gamification Platforms

A number of gamification solutions are available to business and enterprise users. Many of these platforms strive to help the organization improve customer loyalty and brand confidence. Platforms tend to be highly customizable and offer features that integrate game mechanics to drive the desired customer behavior. This typically includes reward systems based on points, badges and special offers for customers who take the desired actions.

The 3 Ms of Gamification (Gartner)

According to Gartner there are three key elements to engage its audience: motivation, momentum and meaning (collectively known as “M3”). Here is how Gartner defines M3:

  • Motivation: Motivation is inspired by most of today’s gamified applications primarily by offering extrinsic rewards and/or weak intrinsic rewards to direct behavioral changes. Extrinsic motivation comes from outside an individual and is inspired by rewards such as money and grades. Intrinsic motivation exists within an individual and derives from that person’s interest in, or enjoyment of, the task.
  • Momentum: Momentum depends on sustained engagement. In gaming, momentum is achieved by balancing the difficulty of the challenges presented with the skill levels of the players. If players find challenges too easy, they will soon get bored. On the other hand, if challenges are too difficult, players will become frustrated. Gamified applications need to engage players quickly and maintain their engagement through deft use of game mechanics such as challenges, rules, chance, rewards and levels.
  • Meaning: Meaning is about serving a larger purpose. Gamified applications must provide rewards that are meaningful to the participants. Different people will find different rewards and incentives meaningful, but many will value opportunities to help charities through donations, lose weight, master a specific skill or achieve a significant task. (source: Gartner)

Source

Advertisements

Tech Terms: CRM Analytics

In CRM (customer relationship management) the term customer analytics —also called CRM analytics — is used to describe an automated methodology of processing data about a customer in order to make better business decisions.
Customer analytics exploits behavioral data to identify unique segments in a customer base that the business can act upon. Information obtained through customer analytics is often used to segment markets, in direct marketing to customers, predicate analysis, or even to guide future product and services offered by the business.
Customer analytics is considered to be a type of OLAP (Online Analytical Processing), a category of software tools that provides analysis of data stored in a database. It is also an important element of a CRM system.

Source

Tech Terms: Cloud CRM

Cloud CRM (or CRM cloud) means any customer relationship management (CRM) technology where the CRM software, CRM tools and the organization’s customer data resides in the cloud and is delivered to end-users via the Internet (see “cloud computing”).

Cloud CRM typically offers access to the application via Web-based tools (or Web browser) logins where the CRM system administrator has previously defined access levels across the organization. Employees can log in to the CRM system, simultaneously, from any Internet-enabled computer or device. Often, cloud CRM provide users with mobile apps to make it easier to use the CRM on smartphones and tablets.

Benefits of Cloud CRM

One main benefit of CRM software delivered in the cloud is scalability. A cloud-based system is designed to be flexible with expanding capacity so a business can scale up (or down) their CRM depending on current business needs. Typically costs of the CRM, which is often based on the number of users and storage requirements also scales up and down as you requirements change. In most cases scaling up is as simple as contacting your cloud CRM vendor and requesting changes to your implementation.

Cloud CRM is often a good choice for small businesses who lack the in-house IT expertise to deploy, manage and upgrade an on-premises CRM application. With Cloud CRM the vendor is responsible for managing the software, providing updates across the system and taking care of technical glitches, bugs and other issues as they arise.

Other benefits of CRM in the cloud include integration with commonly used office applications and email systems, integration with social data (social CRM) and automatic data backups.

Source

Tech Terms: Big Data Analytics

Big data analytics is the process of collecting, organizing and analyzing large sets of data (called big data) to discover patterns and other useful information. Big data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with big data basically want the knowledge that comes from analyzing the data.

Big Data Requires High-Performance Analytics

To analyze such a large volume of data, big data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future.

The Challenges of Big Data Analytics
For most organizations, big data analysis is a challenge. Consider the sheer volume of data and the different formats of the data (both structured and unstructured data) that is collected across the entire organization and the many different ways different types of data can be combined, contrasted and analyzed to find patterns and other useful business information.
The first challenge is in breaking down data silos to access all data an organization stores in different places and often in different systems. A second big data challenge is in creating platforms that can pull in unstructured data as easily as structured data. This massive volume of data is typically so large that it’s difficult to process using traditional database and software methods.

How Big Data Analytics is Used Today
As the technology that helps an organization to break down data silos and analyze data improves, business can be transformed in all sorts of ways. According to Datamation, today’s advances in analyzing big data allow researchers to decode human DNA in minutes, predict where terrorists plan to attack, determine which gene is mostly likely to be responsible for certain diseases and, of course, which ads you are most likely to respond to on Facebook.

Another example comes from one of the biggest mobile carriers in the world. France’s Orange launched its Data for Development project by releasing subscriber data for customers in the Ivory Coast. The 2.5 billion records, which were made anonymous, included details on calls and text messages exchanged between 5 million users. Researchers accessed the data and sent Orange proposals for how the data could serve as the foundation for development projects to improve public health and safety. Proposed projects included one that showed how to improve public safety by tracking cell phone data to map where people went after emergencies; another showed how to use cellular data for disease containment. 

The Benefits of Big Data Analytics

Enterprises are increasingly looking to find actionable insights into their data. Many big data projects originate from the need to answer specific business questions. With the right big data analytics platforms in place, an enterprise can boost sales, increase efficiency, and improve operations, customer service and risk management.

Webopedia parent company, QuinStreet, surveyed 540 enterprise decision-makers involved in big data purchases to learn which business areas companies plan to use Big Data analytics to improve operations. About half of all respondents said they were applying big data analytics to improve customer retention, help with product development and gain a competitive advantage.

Notably, the business area getting the most attention relates to increasing efficiency and optimizing operations. Specifically, 62 percent of respondents said that they use big data analytics to improve speed and reduce complexity.

Source

Tech Terms: Big Data

Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity.

Big Data has the potential to help companies improve operations and make faster, more intelligent decisions. This data, when captured, formatted, manipulated, stored, and analyzed can help a company to gain useful insight to increase revenues, get or retain customers, and improve operations.

Is Big Data a Volume or a Technology?

While the term may seem to reference the volume of data, that isn’t always the case. The term Big Data, especially when used by vendors, may refer to the technology (which includes tools and processes) that an organization requires to handle the large amounts of data and storage facilities. The term is believed to have originated with Web search companies who needed to query very large distributed aggregations of loosely-structured data.

An Example

An example of Big Data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data consisting of billions to trillions of records of millions of people—all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on). The data is typically loosely structured data that is often incomplete and inaccessible.

Types of Business Datasets

When dealing with larger datasets, organizations face difficulties in being able to create, manipulate, and manage big data. Big Data is particularly a problem in business analytics because standard tools and procedures are not designed to search and analyze massive datasets.

As research from Webopedia parent company QuinStreet demonstrates, big data initiatives are poised for explosive growth. QuinStreet surveyed 540 enterprise decision-makers involved in big data and found the datasets of interest to many businesses today include traditional structured databases of inventories, orders, and customer information, as well as unstructured data from the Web, social networking sites, and intelligent devices.

Source

Tech Terms: Structured Data

Structured data refers to any data that resides in a fixed field within a record or file. This includes data contained in relational databases and spreadsheets.
Characteristics of Structured Data
Structured data first depends on creating a data model – a model of the types of business data that will be recorded and how they will be stored, processed and accessed. This includes defining what fields of data will be stored and how that data will be stored: data type (numeric, currency, alphabetic, name, date, address) and any restrictions on the data input (number of characters; restricted to certain terms such as Mr., Ms. or Dr.; M or F).
Structured data has the advantage of being easily entered, stored, queried and analyzed. At one time, because of the high cost and performance limitations of storage, memory and processing, relational databases and spreadsheets using structured data were the only way to effectively manage data. Anything that couldn’t fit into a tightly organized structure would have to be stored on paper in a filing cabinet.

Managing Structured Data

Structured data is often managed using Structured Query Language (SQL) – a programming language created for managing and querying data in relational database management systems. Originally developed by IBM in the early 1970s and later developed commercially by Relational Software, Inc. (now Oracle Corporation).
Structured data was a huge improvement over strictly paper-based unstructured systems, but life doesn’t always fit into neat little boxes. As a result, the structured data always had to be supplemented by paper or microfilm storage. As technology performance has continued to improve, and prices have dropped, it was possible to bring into computing systems unstructured and semi-structured data.

Unstructured and Semi-Structured Data
Unstructured data is all those things that can’t be so readily classified and fit into a neat box: photos and graphic images, videos, streaming instrument data, webpages, PDF files, PowerPoint presentations, emails, blog entries, wikis and word processing documents.

Semi-structured data is a cross between the two. It is a type of structured data, but lacks the strict data model structure. With semi-structured data, tags or other types of markers are used to identify certain elements within the data, but the data doesn’t have a rigid structure. For example, word processing software now can include metadata showing the author’s name and the date created, with the bulk of the document just being unstructured text. Emails have the sender, recipient, date, time and other fixed fields added to the unstructured data of the email message content and any attachments. Photos or other graphics can be tagged with keywords such as the creator, date, location and keywords, making it possible to organize and locate graphics. XML and other markup languages are often used to manage semi-structured data.

Structured Data Technology Standards
SQL has been a standard of the American National Standards Institute since 1986. It is managed by InterNational Committee for Information Technology Standards (INCITS) Technical Committee DM 32 – Data Management and Interchange. The committee has two task groups, one for databases and the other for metadata. HP, CA, IBM, Microsoft, Oracle, Sybase (SAP) and Teradata all participate, as well as several federal government agencies. Both of the committee project documents have links to further information on each project. SQL became an International Organization for Standards (ISO) standard in 1987. The published standards are available for purchase from the ANSI eStandards Store, under the INCITS/ISO/IEC 9075 classification.

Source

Tech Terms: Search Engine Optimization

SEO is short for search engine optimization. Search engine optimization is a methodology of strategies, techniques and tactics used to increase the amount of visitors to a website by obtaining a high-ranking placement in the search results page of a search engine (SERP) — including Google, Bing, Yahoo and other search engines.
Optimizing Visibility in Search Engines
It is common practice for Internet search users to not click through pages and pages of search results, so where a site ranks in a search Play Slideshowresults page is essential for directing more traffic toward the site. The higher a website naturally ranks in organic results of a search, the greater the chance that that site will be visited by a user.

SEO helps to ensure that a site is accessible to a search engine and improves the chances that the site will be found by the search engine. SEO is typically a set of “white hat” best practices that webmasters and Web content producers follow to help them achieve a better ranking in search engine results.

Optimizing Search CTR

SEO is also about making your search engine result relevant to the user’s search query so more people click the result when it is shown in search. In this process, snippets of text and meta data are optimized to ensure your snippet of information is appealing in the context of the search query to obtain a high CTR (click through rate) from search results.

Source

Tech Terms: Search Engine

Search engines are programs that search documents for specified keywords and returns a list of the documents where the keywords were found. A search engine is really a general class of programs, however, the term is often used to specifically describe systems like Google, Bing and Yahoo! Search that enable users to search for documents on the World Wide Web.

Web Search Engines 

Typically, Web search engines work by sending out a spider to fetch as many documents as possible. Another program, called an indexer, then reads these documents and creates an index based on the words contained in each document. Each search engine uses a proprietary algorithm to create its indices such that, ideally, only meaningful results are returned for each query.

As many website owners rely on search engines to send traffic to their website, and entire industry has grown around the idea of optimizing Web content to improve your placement in search engine results. Learn more about search engine optimization (SEO) in this Webopedia’ definition.

Common Search Engine Types

In addition to Web search engines other common types of search engines include the following:

  • Local (or offline) Search Engine: Designed to be used for offline PC, CDROM or LAN searching usage.
  • Metasearch Engine: A search engine that queries other search engines and then combines the results that are received from all.
  • Blog Search Engine: A search engine for the blogosphere. Blog search engines only index and provide search results from blogs (Web logs).

Source

Tech Terms: Computer Keyboard

A computer keyboard is defined as the set of typewriter-like keys that enables you to enter data into a computer or other devices. Computer keyboards are similar to electric-typewriters but contain additional typing keys.

Standard Classification

The standard selection of keys typically found on computer keyboards can be classified as follows:

  • Alphanumeric keys: The standard letters and numbers.
  • Punctuation keys: The comma, period, semicolon, and similiar keys.
  • Special keys: This includes the function keys, control keys, arrow keys, caps Lock key, and so on.
  • QWERTY, AZERTY, Dvorak and Others

The standard layout of letters, numbers, and punctuation is known as a QWERTY keyboard because the first six typing keys on the top row of letters spell QWERTY. The QWERTY keyboard was designed in the 1800s for mechanical typewriters. This layout was actually designed to slow typists down to avoid jamming the keys on mechanical units.

Source

Tech Terms: CPU

CPU (pronounced as separate letters) is the abbreviation for central processing unit. Sometimes referred to simply as the central processor, but more commonly called processor, the CPU is the brains of the computer where most calculations take place. In terms of computing power, the CPU is the most important element of a computer system.

Components of a CPU

The two typical components of a CPU include the following:

  • The arithmetic logic unit (ALU), which performs arithmetic and logical operations.
  • The control unit (CU), which extracts instructions from memory and decodes and executes them, calling on the ALU when necessary.

Printed Circuit Boards, Microprocessors
On large machines, the CPU requires one or more printed circuit boards. On personal computers and small workstations, the CPU is housed in a single chip called a microprocessor. Since the 1970’s the microprocessor class of CPUs has almost completely overtaken all other CPU implementations.

The CPU itself is an internal component of the computer. Modern CPUs are small and square and contain multiple metallic connectors or pins on the underside. The CPU is inserted directly into a CPU socket, pin side down, on the motherboard.

Each motherboard will support only a specific type (or range) of CPU, so you must check the motherboard manufacturer’s specifications before attempting to replace or upgrade a CPU in your computer. Modern CPUs also have an attached heat sink and small fan that go directly on top of the CPU to help dissipate heat.

Source