If you haven’t yet made reservations for tonight, bought flowers or chocolates, or a decadent gift, you *may* still have time. Or you may not. Today, of course, is Valentine’s Day. The day unofficially set aside to shower our romantic partners and loved ones with tokens of love and appreciation – and Americans tend to celebrate with gusto. According to the National Retail Foundation, more than half (52%) of consumers plan to celebrate and will spend an average of $192.80. This is up from $172.41 in 2022, and the second-highest figure since NRF and Prosper started tracking Valentine’s Day spending in 2004. Was your company ready for the influx of consumer spending for this year’s holiday?

Is Enterprise Data Your Valentine?

What is your relationship with your most valuable corporate asset – your data? If you could, would you be sending it flowers and chocolates? Or would you more likely be inclined to contact a qualified counselor to offer couples or one-on-one therapy? If it’s the latter, you aren’t alone.

A newly published report from Accenture and Qlik states that:

  • 60 to 73% of all enterprise data is never analyzed
  • 48% of employees frequently defer to making decisions on gut feeling over data-driven insight
  • 36% of employees state that they would find an alternative method to complete a task without using data

Improve Your Relationship With Data Intelligence

Everyone wants their organization to be data-driven but rarely put in the effort of making it come to fruition. The goal of making all decisions based on accurate, up-to-date information is driving the core of digital transformation efforts, and for that to happen your data needs to be actionable. For your data to be actionable, it needs to be part of an intelligent lifecycle that includes:

  • Data Acquisition & Quality – Data exists in silos. It is divided along departmental and geographical lines. It lives in business applications, legacy systems, and, yes, much too often, spreadsheets. The owners of the data protect it passionately, but, only care about the aspects of the whole picture of a Customer, Product, Location, Account, etc, that they are involved with – and they standardize the data they care about in the way that is most beneficial to their end goals. A Data architecture must first break down those silos and integrate all enterprise information into a single location in which ETL, standardization, deduplication, and stewardship processes can be applied to the data to improve quality and make it ready for use across the enterprise.
  • Master Data Management – At its most basic element, master data is the core set of data elements and attributes that describe the key entities at the heart of an enterprise. A typical organization may rely on data domains like Customer, Product, Asset, Location, Supplier, Part, Account, or more to feed business applications and to make decisions. Mastering that data in an MDM hub that includes business process management, workflows, collaboration, stewardship capabilities, enrichment, relationship modeling, syndication, and multi-channel publication contributes to the overall intelligence of an enterprise architecture.
  • Data Governance – Accurate, up-to-date data needs to be accessible to any user within the corporation who needs access. In addition to ensuring that access rights are applied correctly and upheld, processes should be in place to monitor and approve any changes to key data so that all users receive the same data when making an authorized request. Data Governance processes and tools, like catalogs and approval workflows, are integral to maintaining the health and democratization of your most important asset.
  • Business Intelligence & Analytics – Data platforms require constant monitoring, reporting, and improvement. More is involved than just creating the next AI feature for your customers (although that can be very important). It’s also about viewing real-time, historical, and predictive reports on trends within your organization, your market, and your customer base. Just as the old adage “garbage in, garbage out” was true in the days when Enterprise Data Warehousing was the most popular trend in data management, it is equally applicable to a new paradigm of Big Data and data lakes.

Unfortunately, as with most things, there is no single “silver bullet” to make your enterprise data “intelligent.” Multiple technologies are required, each fitted to your organization’s unique business requirements, Change Management, and most importantly, a careful and well-thought-out strategy and roadmap for success. At Amplifi, we believe in the power of intelligent data, and our team of consultants and technology partners can help you love your enterprise data again.  Maybe next Valentine’s Day you will send it flowers. Contact us today to find out how we can get you there.