What People Say About Us
Matt Edwards Senior Oracle Consultant
I’ve collaborated closely with Rich for over 10 years on numerous Oracle database projects for major financial institutions in New York City. Rich has always been able to resolve complex issues and to provide quick insights whenever needed. Additionally, our data integration and migration efforts have always succeeded in a timely manner due to Rich’s insights and supervision. Whether developing customized solutions or working directly with various software vendors and technologies Rich is able to guide projects to completion. “
Big Data can be used to boost recruiting and employee retention. Over the past couple of years, human resources and recruiting have found that using analytics helps target the right talent much the same way an ideal customer profile helps sales and marketing. There is a huge amount of information already available inside your company, just waiting to be turned into actionable data. Data can help determine what are the right criteria to include in a job listing to attract the best candidate and what are the right skills that will lead to a satisfied, long term employee. Data will help you better identify your most cost-effective talent sources and which produce the best outcomes.
Workforce enhancement and HR Analytics are exciting new areas that can bring big returns for companies. Just like customer satisfaction, employee satisfaction improves retention and increases productivity. Big Data be used to boost productivity through better bonus programs, for one. Behavioral scientists have studied what types of incentives generate the highest engagement based on personality types. And when more employees are engaged in achieving their goals, overall productivity increases. HR Analytics can go beyond traditional performance metrics, to suggest key performance indicators that can identify talent and forecast dissatisfaction. Metrics can then be mined to identify patterns and identify issues in advance. Big Data can also be applied to improve staff learning, improving training outcomes and results that impact productivity.
If you’ve mined your employee Big Data to forecast dissatisfaction, then you can proactively reach out to employees to uncover areas for development before they exit. Data analytics can give a true for benchmark performance and reveal the gaps between your top performer and your bottom ones. A better understanding of performance can help people improve and close that performance gap. Data scientists who specialize in managing a variety of data from many sources – structured and unstructured – can be a help in pulling together and extracting meaning from this type of project.
Your best customers aren’t always your biggest ones. And small, high churn customers can be a drag on support and overhead. Customer profitability analysis can be used in several ways. By understanding who are your most profitable customers you can better manage your revenue flow, improve margins, manage overhead expenses, and prioritize support and development efforts to address their specific needs. It also allows you to build an accurate ideal customer profile and buyer personas that enable sales and marketing to focus your acquisition spend and activities on targeting more of the right customers.
Today, SaaS models mean that customer retention is more of a priority than ever since you need to get users to renew each year, over and over again. Understanding what customers will churn and what ones are loyal is essential. One indicator of retention is usage. Sales, marketing, and product managers need data that gives them a meaningful way to measure product usage and develop predictors of whether prospect will buy or a customer is likely to stay or go. Today, it’s essential to tie product engagement to dollars in the long term. Understanding individual usage as well as account penetration will help shorten sales cycles, improve retention, and build better long-term revenue forecast pictures.
As any sales manager knows, it’s a huge challenge to get a reliable sales revenue forecast out of your CRM. By using data analysis, this can be improved dramatically. For one, once you have built a reliable ideal customer profile, then you can analyze which prospects are the best match, which has impact on the sales cycle duration, close rate, as well as probability if they are on target. You can also run historic analysis to better understand what types of deals did or didn’t close in the past to build a predictor of the future. Couple that with demand planning and you can turn your CRM from the equivalent of a Magic 8 ball, to a smooth-running engine that operations can rely on for planning.
If you have decided it’s time to move your on-premises Oracle, SQL Server, MySQL, MariaDB, or Postregre SQL database, you are not alone. Cloud environments are scalable, reliable, and highly available. However, to ensure that you achieve the greatest benefit from your migration, whether that is meeting increasing demand, reducing operational costs, your business is expanding to more geographies, or you want to set up a Cloud disaster recovery system. Regardless of your goals, it’s important to create a solid plan, have a way to convert your schemas and stored procedures, and then test the results. And before you start, this is a good time to evaluate your current databases and information stores to make sure you’ve got all the data in one place that you want to migrate.
No matter how big or small or company is, loss of critical data can be costly – in terms of revenue lost or reputation damaged. The complete loss or compromise of essential systems can have a huge effect on your company’s ability to do business. In most circumstances you can’t just “turn off” the data flow when a system goes down. If you don’t capture the data it is lost, compounding the problem. The best way to ensure this doesn’t happen is to have a solid, well laid out business continuity and disaster recovery plan in place.
Big data and analytics can improve efficiency in operations, manufacturing, and your supply chain. Capturing customer data could mean more insights for product development or support services. Data from production and distribution could mean more timely decisions that lead to faster delivery and more satisfied customers. And all of the data now coming off of sensors and IoT devices need to be managed and made available to teams company-wide to use in making their processes more efficient, saving time and optimizing their output.
The growing numbers of regulations across industries such as banking, life sciences, healthcare, and manufacturing generate a vast amount of structured and unstructured data. Beyond just analyzing compliance trends, this can be used for many other purposes as well. Insights can be gained from using predictive analytics on the data harvested from hospitals and other health services providers from accident reports, disease center reports, and social service case files. These data can be used by a number of different agencies to do everything from pinpointing the source of disease outbreaks, to determining where to assign more health or emergency services, to informing agency policy. These are just a few examples of the data collected as part of the regulatory process that can be used to provide value and deliver rich insight.
Consumer fraud is a big concern for many government agencies, such as concerns over fraudulent payments through Medicare and Medicaid or the Department of Education Federal Student Aid systems. Predictive analytics can successfully mitigate these types of challenges. However, government agencies face scrutiny to keep the costs of new projects low. Careful project planning and an iterative approach are essential to keeping project costs in control. Programs such as the Insight Data Solution Discovery Program help agencies understand the scope of their problem so that they can budget and plan accordingly.
While online data collection is popular, other data collection and survey methods such as observation, mobile or paper surveys, document analysis, or live interviews, can lead to richer insights or provide access to subjects in hard to reach areas. It’s important to establish clear goals for the purpose of the evaluation and the types of information you want, which will help determine the best mix of survey and data collection methods to accomplish your goals. Insight Data Solutions has years of experience in developing unbiased survey methods and data collection to reach your objectives.
Insight Data Solutions has the know-how and expertise to help you leverage Big Data for your people, sales, and operations. Read about our Services.
Are you ready for an Insight Data Solutions Data Discovery to find out how you can transform your business or agency with new insights?