- Many companies still struggle to extract insight from their data.
- Generally, as a smaller business you have the ability to be more nimble.
- You need to treat data as a tool for growth, not a cost center.
- Moreover, you need to focus on what type of insight will help your business to grow.
Over the past five years, big data, machine learning and artificial intelligence (AI) have become hot topics in the business world. Unlike the management fad du-jour, the capabilities of AI have transformed some of the most difficult and outdated business processes into highly efficient and profitable systems. But the gains from AI have also prompted widespread questions about whether fast-developing machine learning technologies can be applied to make small businesses even more efficient in other areas such as increasing sales.
Solving the problem of gleaning insight from data
Most organizations rely on AI to generate customized reports, data and customer insights. But their ability to measure, manage and control these efforts is limited. This is precisely why AI is still most often associated with cognitive applications and big data. Only a small proportion of new AI initiatives are aimed at companies’ operational processes, such as processes that increase sales. This leaves plenty of opportunity for real-time data optimization, more effective sales and reduced costs.
Until now, business intelligence has largely been the realm of the “smart humans” that think their way around spreadsheets and databases. But AI offers a powerful and easy-to-use way of capturing and showcasing this data.
The challenge is that the amount of data available is vast. And despite the movement toward open source database technology, when it comes to data analysis it’s the proprietary (and mostly unavailable) company-created data from big businesses that customers flock to. This is particularly true for customers with big data programs. And much of the data in the systems big data companies have developed will become very expensive to maintain as those companies mature. A small company, though, can save itself some money by embracing big data and doing its own analysis.
This is particularly true of small companies that are struggling to take advantage of the tools already available. But it’s also true of the biggest companies that are joining big data adoption because they realize that they are missing a great deal of competitive advantage. Small companies aren’t the only players who might benefit. Big companies need to start thinking about how big data can add value to their own businesses. But they need to wrap their heads around what this data will mean for their company.
Small businesses gain an advantage in being nimble
Companies need ways to mine and interpret all the data they collect, while simultaneously improving products and services, acquiring leads and converting prospects into paying customers.
However, for many there is the internal obsession with controlling everything and ignoring what is right in front of them. This lack of a clear business strategy leads to endless consultations, disagreements and wasted resources. Many organizations that are already equipped with AI tools often fail to make the most of them.
Smaller companies face significant challenges when developing AI technology, due to the relative challenge of digitizing disparate processes. Many small businesses struggle to overcome the time and cost of human interaction to simplify, and potentially automate, their operations. However, when used in conjunction with data analysis and data visualization, these tools can assist with understanding a company’s processes and trending to establish a greater understanding of the business.
Companies need to understand these challenges, establishing innovative, open and effective techniques while building internal community. This approach doesn’t take long. Once you’ve set goals and set some expectations, you’re well on your way to taking advantage of data in your organization.
Treat your data as a driver of growth, not a cost
There is plenty of evidence that companies that invest in big data reap a lot of benefits. After all, the opportunities presented by analysis of customer data can be profit-boosting or even life-saving.
You can understand why companies might want to think of big data and technology as a service. This is typically true even for startups that might not yet have a product or know exactly what they have to offer. Start-ups simply have less capital to invest in the technology and services required to deliver on their innovation.
For those companies, investing in technology is also an act of belief in their ability to deliver on that promise. Technology should eliminate a lot of the tedious and costly manual conversion and supply chain optimization work so businesses can focus on creating awesome experiences for their customers.
Any kind of big data effort needs to look at the data it has already collected to understand what trends and causes it has already identified, spot new possibilities, determine correlations and understand possible outcomes. This approach allows marketers to better understand consumer behavior, preferences and emotions, but anyone in business should think about the opportunity it affords.
That means having a hard conversation about how to maximize the potential for the information you have created. One of the first questions you should ask yourself is whether big data is really a service or technology. This is a pretty good indicator of whether the company itself will benefit from it.
In a competitive industry with legacy companies encumbered by technology, AI powered platforms promise to save on data protection and load dramatically. Achieving digital transformation takes money, time and a keen eye. AI enhanced software products gives us advanced capabilities such as machine learning, not only to create smarter offerings but also to expand partnerships and enhance our overall service offering to our clients.
Beginning with Business Intelligence
Business Intelligence (BI) is about data and information, but interestingly, the technology behind it is often overlooked. What’s still missing is the ability to understand how a business is using its data in real time and incorporate this insight into the system so the business can quickly make decisions. A company’s transactional systems provide a valuable data base, but this is raw information at best. And the BI systems that work with these systems often don’t really create value out of them.
Business intelligence is one of the most important tools that can help employees navigate through the complex organizational challenges which arise in their daily work. They are able to analyze large amounts of data while unearthing useful insights from the data available.
Business intelligence is therefore a great way for employees to gather the information they need to make smarter decisions throughout their day. It is far more accurate, in that regard, than mass search engines, which present pre-determined search terms and results. This means that both employees and managers can make sure that they make the right decisions with their business and earn the right to work.
With machine learning (ML), we’re finally getting to the point where a system can understand the business context and preferences of an organization. This will help identify when an employee’s behavior is potentially impacting the business and the systems needed to work in concert to prevent it.
The good news is that ML systems are getting cheaper, and these are some of the fastest moving trends in tech, enabling BI systems to handle more data in minutes. Technology companies like Microsoft and Google are making strides in adding this capability, as are BI vendors like Salesforce and ServiceNow, by building learning into their systems and even the underlying data pipelines.
Your CRM as a window into the customer’s thoughts
Customer relationship management is a huge industry in itself. Companies spend billions on a wide range of CRM technologies: people, systems, tools, processes and, of course, humans. And when they spend money, they want to know where their money is going, what options are available and how effective they are at driving customer acquisition and retention.
By the time companies move from having to manage their own existing and newly acquired data, gathering social media profiles and to AI customer success functions, they have a wealth of information that they never knew they had. This includes customer interactions to date, of all kinds. This can provide a large trove of valuable insight.
Getting started with data insights
If you have data that you can access, you’re already halfway there. Now you just need to figure out exactly what types of insights will help your organization. For instance, you might want to learn more about how many customers developed a knowledge of your product after the initial purchase and how the organization was able to retain them on your platform. Additionally, if you’re going to be communicating data to your stakeholders and customers, your people need to be able to access those data sets and use them in various ways that fit their functional styles and expertise.
Creating a template for your organization may seem like a simple process, but make sure you’re working with the right tool to do so. Starting a project of this kind will also require a test, which is an important part of the planning process. Use a test drive to see how the software performs over a specified time period and scope of time. Whatever tools you use, make sure to use an amount of experience, determination and patience which will enable you to properly analyze and respond to the latest movements in the market.
The next big thing for your data
There’s an aging workforce and a generational divide that does not feel conducive to the need to catch up with the pace of innovation. To really speed up the change curve, you need to provide better outcomes for training and retraining the workforce.
The world of software development and architecture will need to move faster than currently thought possible, so businesses will need to have an increasing level of technical expertise among their employees. There is also a growing demand for people to understand the role of data scientists, so that the organizations that hire them can plan accordingly.
Companies that harness the power of machine learning to transform the way they collect, analyze and present large amounts of data should be rewarded with better insights.