oday, phones converse with us, cars drive themselves, and smart appliances save us energy by learning our behaviors. Rapidly advancing technology is changing how we live our lives, but what does this mean for businesses?
Regardless of industry, businesses must embrace technology that leads to real advantages over the competition. In the past, only large corporations had the resources to leverage powerful new technological concepts. Now, falling software development costs make integrating those concepts possible for any business.
More and more companies are applying major technical advances into their business model. The competition is developing fast.
Every enterprise needs to understand the implications these topics will have on your business today, or risk falling behind tomorrow.
How big is Big Data? Ultimately, it’s a vague term that encompasses all the information your business generates. With the right analysis on top of it, companies can glean powerful insights into their firm’s operational efficiency, marketing effectiveness or other KPI’s. Whether this includes results from an email campaign, mentions on social media and chat platforms, or purchase behavior, it’s all tangential to the future of your success.
Gartner defines Big Data with the three Vs: Volume, Velocity, and Variety. The amount of data funneling into your business (volume) doesn’t necessarily come in at the same speed (velocity). There are fits and bursts; maybe your marketing team runs a paid search campaign that generates lots of leads. Maybe the product team releases new features in a steady cadence. Or perhaps the press covered your company and prospects are chomping at the bit to become customers. This of course brings us to the last V, variety. Your data can come from a variety of sources, not just digital channels (such as CRM, customer feedback, financials, etc).
Gone are the days when a major strategic decision is based on a gut feeling. When armed with the information and insights Big Data provides, every major decision is well informed and more likely to be the right decision.
Natural Language Processing (NLP)
Natural Language Processing is a field within computer science that enables computers to understand language as it’s spoken. It’s one facet that helps make machine learning and artificial intelligence possible.
NLP simplifies customer service with automated online assistants, or bots, to handle questions prospects or customers ask on your website. It can also be used in sentiment analysis, or simply gauging what people say about your business or product online. Monitoring your reputation is critical for the success of your business, and the best way to understand how your target audience feels is to use NLP to listen to them. In a similar way, NLP can help you get a sense of what’s happening with your competitors. News headlines can be parsed into digestible information automatically. Company acquisitions and change in leadership can be recognized instantly using NLP.
AI assigns meaning to your company data and uses that to automate and streamline processes. You’re probably most familiar with machine learning within tools like Amazon’s Alexa and Apple’s Siri. You simply make a request and your words are turned into search queries or commands that a machine can digest, identify relevant information, and serve up that information in response.
Many companies have already deployed AI-powered chatbots within their organization so employees can receive instant answers that apply to their specific role, exemption status, and more. It’s a great first step, but that’s only the tip of the AI iceberg.
AI can be used in every part of a business. Customer support or success teams can aggregate insights and recommend content that will help users maximize the value of your product or service. AI continues to grow in popularity in the financial investment sector by automating trend analysis, investor risk tolerance, and additional factors lead to more accurate decisions. Even HR departments can take advantage of AI to automate the mundane FAQ they are inundated with every day. The possibilities are endless.
AI can enhance how every aspect of your company operates and collaborates. When the entire business runs on AI, the efficiency gained improves both your top and bottom line: employees are satisfied with how easy processes and tasks become, and the time and resources saved translates into real savings.
Machine Learning (ML)
Essentially, machine learning is the process of feeding a machine (like a computer) data that helps it learn without explicit programming. Most importantly, ML adds the most value through its ability to recognize patterns. Organizations can extract insights from the patterns identified by ML to better inform strategy moving forward.
A more beneficial business use case comes from Zendesk, who used ML to recognize patterns in contact data in order to build a more precise idea of buyer personas. Additionally, comments on your company’s blog of forum can be analyzed by ML to determine if there are gaps in your audience’s understanding of your product, or needs you might have overlooked. Much like Netflix analyzes your what you watch to recommend what you should binge next, ML can upsell other products or services your company offers to existing customers.
Since machine learning does so well with patterns, it can be used to alert you when something out of the ordinary occurs. Whether there’s been an irregular number of uninstalls of your product, downtime of your servers, or a decline in manufacturing speed, ML recognizes the occurrence as an anomaly, which allows you to act proactively to fix the situation, rather than react when customers complain.
Predictive analytics is all about using data you already have to predict how future events will play out. The idea encompasses a lot of methods to get the insights you’ll need to light the way ahead: AI, ML, data mining, and statistics. This methodology is having a huge impact on financial technology currently. It’s very also useful when applied to customer behavior and retention, forecasting inventory, and optimizing marketing campaigns.
The goal of predictive analytics, machine learning, and AI is a “continuous stream of new learnings and insights,.” For instance, aggregating and assessing customer data will help your sales team predict churn, a major factor in your bottom-line. As many businesses adopt agile processes, predictive analytics can be run on every iteration of your software to anticipate failures, errors, or other negative outcomes that would translate into unhappy prospects or customers.
It would take a crystal ball to predict the future with certainty, but predictive analytics will get your business pretty darn close.
When it comes to enterprise technology today, it is not business as usual. Companies of all sizes are realizing the value their company’s data sources provide, through AI, ML, and NLP. For those decision makers on the fence to invest, now is the time to strike. The largest companies have already proven the effects of IT modernization on the bottom line and the long tail is about to join in. Modernizing your business practices today gives you a competitive advantage. Wait a few years, and your company may never catch up.
Popular Project Management Software Options
JIRA by Atlassian is one of the most popular and comprehensive project management tools on the market, and is a leading software development app for agile developers. Licensing is very affordable up to 10 users, then makes a significant jump in cost if you need 11 or more.
Basecamp is another popular web-based project management tool that began as the very first Ruby on Rails app. Basecamp charges a flat fee for business, regardless of the number of users. It integrates with a variety of other applications and services.
Google Sheets offers a free template for more cost-conscious organizations. Simply set up a Google account and invite other users to contribute. Functionality is limited, but works fine for SME’s looking for the ability to centralize issue tracking.