Building Your Data Game Plan for 2020

By 2020, businesses using data will see $430 billion in productivity benefits over competitors who are not using data. – The International Institute for Analytics

January can be a great time to get clear on goals, expectations, and desired results before diving back into work. It’s also an ideal time to look back on what didn’t pan out, and what aspects of your strategy proved to be a waste of time.

 

Out with the Old

This is ‘step zero’ of structuring your data game plan for 2020. Before new initiatives can be enacted, organizations need the awareness to pull out of projects that aren’t working out – or at least redefine their parameters. Having valuable money and resources tied up in projects that are seeing no tangible results will dilute your success, even if you’re equipped with a winning game plan for the upcoming year.

So before doing anything else, consider the sunk cost fallacy as it pertains to your analytics strategy: Just because you’ve invested time, money, and talent in a project doesn’t mean you should continue it out of obligation. Cut ties with data projects that fail to provide meaningful insights you can act on. With this knowledge, you can move into 2020 with a clean slate and more space for new ideas.

Pinpointing Big-picture Goals

“The word priority came into the English language in the 1400s. It was singular. Only in the 1900s did we pluralize the term and start talking about priorities. Illogically, we reasoned that by changing the word we could bend reality. Companies routinely try to do just that. This gave the impression of many things being the priority but actually meant nothing was.”
Greg McKeown

Time is finite. The more priorities you choose, the less time you have to spend on each priority. An organization that struggles to narrow down its goals can become a jack of all trades and a master of none. Grinding along like this will make results seem impossibly slow.

Instead of trying to juggle it all, a high priority goal with fewer challenges standing in the way may be the right focus for the time being. Some big-picture goals might be increasing online presence, customer retention, or shareholder value.

Translating Goals into Analytics Tasks

 

It sounds like a cliché productivity tip, but breaking down big-picture goals into achievable tasks is critical, especially with something as complex as data analysis. Most organizations know what they’re supposed to do: collect data and attempt to put it to good use. But when data go to waste, it’s almost always because of a lack of follow-through.

 

Oftentimes, what seems simple in theory becomes a huge mess of incompatible data that leaves teams confused and stressed. Teams often launch into projects that are meant to define their sales strategy for the year, only to find the project takes triple the anticipated time or unforeseen tech issues prevent it from happening at all.

Harvard Business Review suggested teams follow these rules to stay on task and extract real value from their data:

 

  1. Use simple models and “focus on reducing the time between the data acquisition and model development.
  2. Explore more business problems quickly rather than exploring one with a highly complex model.
  3. Gain insights from samples of data rather than all the data.
  4. Automate data processing techniques rather than re-doing them manually.

 

The more organizations can simplify and streamline tasks, the easier it is for team members to stay focused on the main objective. To do this, you’ll need assistance from digital tools.

 

Identifying Necessary Technology

 

We emphasize the critical strategy of saying ‘No.’ Not every team needs to be doing AI. Analytics is not a race to be the most advanced or most mature. Avoid chasing trendy technologies that may have little business impact.” – Donald Farmer of TreeHive Strategy

With the plethora of data tools available today, it’s so easy to invest in something fancy you don’t actually need yet. Many knowledge hubs for businesses share the ‘top data platforms’ or the ‘best analytics tools,’ but few help you distinguish between what you personally need and what is irrelevant.

 

When it comes to data tools, a few key considerations can help you single out the right technology. Most major data platforms include standard scalability, security, visualization, and integration features. So instead of looking for tools with the most features, think about what you’ll need the tools to do. How can it help you execute tasks?

Source: https://medium.com/@liyenz/types-of-analytics-649acafe8966

 

Data Education & Security  

Lastly, its crucial that organizations stay on track with self-education. In other words, building a company culture that values data and at least loosely understands it will be vital as the workplace becomes increasingly data-driven. Of course, not all team members need the same level of understanding depending on their role. But when analytics goals and plans are not communicated to team members regularly, this is when silos can disrupt workflow significantly. In addition, teams that don’t have a strong culture of data education are more likely to be unaware of compliance and data security issues, which are expected to be an even bigger theme in 2020.

 

Information is what ultimately moves organizations toward a better future, and data is the “how” in that equation. Equipped with a strong data game plan for 2020, any type of organization can succeed more quickly than ever before.