What level of management that functions are highly unstructured and resources are undefined?

Demand might be good for your product or service, but if you can’t efficiently deliver what you’re selling, you can’t maximize your sales. A poor organizational structure not only hurts your ability to maximize opportunities, but can also create problems can lead to serious financial consequences. Understanding the impact of a poor company structure will help you take steps to ensure your internal management operations let you take your business to the next level.

Table of Contents Show

Organizational Structure
Poor Communication
Lack of Strategic Management
Reduced Productivity
Damaged Company
What is structured data?
Pros and cons of structured data
Structured data tools
Use cases for structured data
What is unstructured data?
Pros and cons of unstructured data
Unstructured data tools
Use cases for unstructured data
What are the key differences between structured and unstructured data?
What is semi-structured data?
The future of data
Structured and unstructured data and IBM

Organizational Structure

Organizational structure refers to the assignment of management duties and how you organize your various functions. Many small businesses start with a flat organizational structure, which has a few key employees reporting to and working directly with the owner. As companies grow, they often create hierarchical corporate structures, which include departments, executives, managers and subordinates. They then create organization charts that show the totem pole, both in terms of functions and employees.

Poor Communication

A poor organizational structure can lead to miscommunication because people might not be sure who needs information or where to send important messages. For example, a sales manager at a small company might create a new customer sign-up form to help new clients start ordering more easily, getting approval from the owner of the company to use the form. If the new form isn’t shared with the bookkeeper, she might not know how to qualify new customers for credit or invoice them properly. This can lead to delayed orders and long waits for payment from clients. This also can result in customers not getting product, forcing them to find another supplier.

Lack of Strategic Management

To maximize your growth potential, you’ll need to make plans for the future that create opportunities, rather than simply wait for new business. Without an organizational structure that puts key executives or employees together on a regular basis, it will be difficult to create effective long-term strategies. Strategic management includes creating growth through objectives such as introducing new products, using new distribution channels, expanding geographically or going after a new target market. These strategies often require input from your marketing, accounting, information technology, production and sales managers.

Reduced Productivity

When your organization isn’t set up to create effective communications or doesn’t foster creative strategic management, you can miss opportunities, allow problems to continue and reduce employee morale. An employee who has two superiors might get conflicting directions and not want to follow either until she gets approval from higher up. When employees aren’t happy, they might not volunteer new ideas or might leave for a better job. Poor organizational structure can lead to a reduction in productivity, decreasing your growth potential. For example, when sales people don’t communicate with production managers about large orders they know will be coming in the months ahead, the production department can’t make the most effective scheduling plans, leading to a lack of production capacity, labor availability, warehouse storage space and shipping efficiencies.

Damaged Company

In addition to the external frustration you can experience with a poor organizational structure, you can create problems for your customers when they don’t get timely answers to their questions, receive poor customer service or have to wait longer than normal for delivery of product. When this happens, you might lose customers, lose references and the ability to attract new customers and reduce revenues enough to damage your business. Once you start missing payroll, can’t repair equipment and machinery or start racking up a credit card, you create a downward spiral that’s hard to turn around.

All data is not created equal. Some data is structured, but most of it is unstructured. Structured and unstructured data is sourced, collected and scaled in different ways, and each one resides in a different type of database.

In this article, we’ll take a deep dive into both types so that you can get the most out of your data.

What is structured data?

Structured data — typically categorized as quantitative data — is highly organized and easily decipherable by machine learning algorithms. Developed by IBM in 1974, structured query language (SQL) is the programming language used to manage structured data. By using a relational (SQL) database, business users can quickly input, search and manipulate structured data.

Pros and cons of structured data

Examples of structured data include dates, names, addresses, credit card numbers, etc. Their benefits are tied to ease of use and access, while liabilities revolve around data inflexibility:

Pros

Easily used by machine learning (ML) algorithms: The specific and organized architecture of structured data eases manipulation and querying of ML data.
Easily used by business users: Structured data does not require an in-depth understanding of different types of data and how they function. With a basic understanding of the topic relative to the data, users can easily access and interpret the data.
Accessible by more tools: Since structured data predates unstructured data, there are more tools available for using and analyzing structured data.

Cons

Limited usage: Data with a predefined structure can only be used for its intended purpose, which limits its flexibility and usability.
Limited storage options: Structured data is generally stored in data storage systems with rigid schemas (e.g., “data warehouses”). Therefore, changes in data requirements necessitate an update of all structured data, which leads to a massive expenditure of time and resources.

Structured data tools

OLAP: Performs high-speed, multidimensional data analysis from unified, centralized data stores.
SQLite: Implements a self-contained, serverless, zero-configuration, transactional relational database engine.
MySQL: Embeds data into mass-deployed software, particularly mission-critical, heavy-load production system.
PostgreSQL: Supports SQL and JSON querying as well as high-tier programming languages (C/C+, Java, Python, etc.).

Use cases for structured data

Customer relationship management (CRM): CRM software runs structured data through analytical tools to create datasets that reveal customer behavior patterns and trends.
Online booking: Hotel and ticket reservation data (e.g., dates, prices, destinations, etc.) fits the “rows and columns” format indicative of the pre-defined data model.
Accounting: Accounting firms or departments use structured data to process and record financial transactions.

What is unstructured data?

Unstructured data, typically categorized as qualitative data, cannot be processed and analyzed via conventional data tools and methods. Since unstructured data does not have a predefined data model, it is best managed in non-relational (NoSQL) databases. Another way to manage unstructured data is to use data lakes to preserve it in raw form.

The importance of unstructured data is rapidly increasing. Recent projections indicate that unstructured data is over 80% of all enterprise data, while 95% of businesses prioritize unstructured data management.

Pros and cons of unstructured data

Examples of unstructured data include text, mobile activity, social media posts, Internet of Things (IoT) sensor data, etc. Their benefits involve advantages in format, speed and storage, while liabilities revolve around expertise and available resources:

Pros

Native format: Unstructured data, stored in its native format, remains undefined until needed. Its adaptability increases file formats in the database, which widens the data pool and enables data scientists to prepare and analyze only the data they need.
Fast accumulation rates: Since there is no need to predefine the data, it can be collected quickly and easily.
Data lake storage: Allows for massive storage and pay-as-you-use pricing, which cuts costs and eases scalability.

Cons

Requires expertise: Due to its undefined/non-formatted nature, data science expertise is required to prepare and analyze unstructured data. This is beneficial to data analysts but alienates unspecialized business users who may not fully understand specialized data topics or how to utilize their data.
Specialized tools: Specialized tools are required to manipulate unstructured data, which limits product choices for data managers.

Unstructured data tools

MongoDB: Uses flexible documents to process data for cross-platform applications and services.
DynamoDB: Delivers single-digit millisecond performance at any scale via built-in security, in-memory caching and backup and restore.
Hadoop: Provides distributed processing of large data sets using simple programming models and no formatting requirements.
Azure: Enables agile cloud computing for creating and managing apps through Microsoft’s data centers.

Use cases for unstructured data

Data mining: Enables businesses to use unstructured data to identify consumer behavior, product sentiment, and purchasing patterns to better accommodate their customer base.
Predictive data analytics: Alert businesses of important activity ahead of time so they can properly plan and accordingly adjust to significant market shifts.
Chatbots: Perform text analysis to route customer questions to the appropriate answer sources.

What are the key differences between structured and unstructured data?

While structured (quantitative) data gives a “birds-eye view” of customers, unstructured (qualitative) data provides a deeper understanding of customer behavior and intent. Let’s explore some of the key areas of difference and their implications:

Sources: Structured data is sourced from GPS sensors, online forms, network logs, web server logs, OLTP systems, etc., whereas unstructured data sources include email messages, word-processing documents, PDF files, etc.
Forms: Structured data consists of numbers and values, whereas unstructured data consists of sensors, text files, audio and video files, etc.
Models: Structured data has a predefined data model and is formatted to a set data structure before being placed in data storage (e.g., schema-on-write), whereas unstructured data is stored in its native format and not processed until it is used (e.g., schema-on-read).
Storage: Structured data is stored in tabular formats (e.g., excel sheets or SQL databases) that require less storage space. It can be stored in data warehouses, which makes it highly scalable. Unstructured data, on the other hand, is stored as media files or NoSQL databases, which require more space. It can be stored in data lakes which makes it difficult to scale.
Uses: Structured data is used in machine learning (ML) and drives its algorithms, whereas unstructured data is used in natural language processing (NLP) and text mining.

What is semi-structured data?

Semi-structured data (e.g., JSON, CSV, XML) is the “bridge” between structured and unstructured data. It does not have a predefined data model and is more complex than structured data, yet easier to store than unstructured data.

Semi-structured data uses “metadata” (e.g., tags and semantic markers) to identify specific data characteristics and scale data into records and preset fields. Metadata ultimately enables semi-structured data to be better cataloged, searched and analyzed than unstructured data.

Example of metadata usage: An online article displays a headline, a snippet, a featured image, image alt-text, slug, etc., which helps differentiate one piece of web content from similar pieces.
Example of semi-structured data vs. structured data: A tab-delimited file containing customer data versus a database containing CRM tables.
Example of semi-structured data vs. unstructured data: A tab-delimited file versus a list of comments from a customer’s Instagram.

The future of data

Recent developments in artificial intelligence (AI) and machine learning (ML) are driving the future wave of data, which is enhancing business intelligence and advancing industrial innovation. In particular, the data formats and models covered in this article are helping business users to do the following:

Analyze digital communications for compliance: Pattern recognition and email threading analysis software that can search email and chat data for potential noncompliance.
Track high-volume customer conversations in social media: Text analytics and sentiment analysis that enables monitoring of marketing campaign results and identifying online threats.
Gain new marketing intelligence: ML analytics tools that can quickly cover massive amounts of data to help businesses analyze customer behavior.

Furthermore, smart and efficient usage of data formats and models can help you with the following:

Understand customer needs at a deeper level to better serve them
Create more focused and targeted marketing campaigns
Track current metrics and create new ones
Create better product opportunities and offerings
Reduce operational costs

Structured and unstructured data and IBM

Whether you are a seasoned data expert or a novice business owner, being able to handle all forms of data is conducive to your success. By leveraging structured, semi-structured and unstructured data options, you can perform optimal data management that will ultimately benefit your mission.

To better understand data storage options for whatever kind of data best serves you, check out IBM Cloud Databases.