Global Data Science Forum

Top 5 data science and strategy predictions from German Influencer Marketing Agency - Audiencly

By Bruce Wilson posted Wed January 15, 2020 10:28 AM

Research from the influencer marketing agency points that more than half of companies (53%) want to use data and analytics to increase process performance within their strategy, with improving client experience and new product growth tied in second place (indicated by 29% of respondents).

Today businesses are looking at various ways to leverage data as a way to discover creative, actionable insights – with the final goal being to outpace the fierce conflict and drive differentiation.

As we’re now entirely and truly in the age of data, it’s essential to recognize what’s next.

1. “Data knowledge” will get on the buzz of agile methodologies

In 2001 the agile methods began its rise from the world of software development to comprehensive project management leadership, promoted as the way to cope with endless change. It mixes disciplined performance with continuous change in ways that energize workers involved. One of the residents of agile is the capacity to crack down organizational siloes. But at a point where reform is (or should be) highly technological and data-driven, without the own language in which to communicate, those from business-focused roles will not contribute to the discussion in the same way as the data-savvy.

Accordingly, data literacy will appear as mission-critical for companies looking to innovate around the increasing volumes of data continuously collected. A variety of thought is accepted as key to high discovery capabilities, from the emergence of tools that allow everyone across organizations with the experience to ideate and group around changes they hold. Data literacy will be regarded as the facilitator of this ideal situation; non-technical workers will be able to explain their proposals to the data scientists and experience barriers to the realization of their ideas.

2. People will stop speaking about big data, but business data strategy will still be a top preference for enterprises, backed by the majority of the CDO role.

85% of the respondents in Exasol’s 2019 Cloud Survey own a Chief Data Officer (CDO) within their business, which shows and sets the status of data plan as a mission-critical force in industry today.

But in 2020, we’ll see the phrase ‘big data’ drift off as businesses mature beyond this buzzwordy vocabulary. Instead, they will have special use-case terms to raise their data analytics purposes. For example, alternatively of saying “we do big data”, they will say “we’re running with customer demographics, credit card bills, transactions and point of sale data, online and mobile orders and payments, and credit bureau data to find similarities to define tens of thousands of micro-segmentations in the client base. We then build ‘next goods to purchase’ rules that increase sales and buyer retention.”

3. The balance of CDO’s in financial assistance institutions will exceed other activities as the role transforms quickly

CDO offices have risen dramatically in the last two years but more so in financial assistance organizations. In 2020, we await CDO jobs to be more common in FSI, over other manufacturers, as they formalize and commit to completing an office of the CDO.

As one of the most analytically advanced sectors, the CDO role in the FSI industry is transforming, moving from its original technical roots to encompass a broad agenda that spans data management, analytics, data science, ethics and digital change. More importantly, CDOs are practicing their high profile and central role to act as change means for the business, concentrating more on business contact and value realization.

While the CDO’s principal duties have focused on regulatory agreement and operationalizing regulatory charges, leading FSI organizations are using the environment as an enabler of business insights, policies, and reforms, such as increasing value-adding data services that are approved by the new foundational methods and policies.

4. Popular/general selection of artificial intelligence will only rise in the most superior firms

We will proceed to see AI ventures gather speed, but for most companies, this will only be in small use-cases that enable them to pick off the low-hanging products in their businesses. For example, CPG companies are more likely to spend in physical robotics for the factory floor, and telcos will fund in customer-facing virtual instruments.

The top players will look to use AI to generate value more broadly across business lines and functions. For example, sentiment analysis can be used not only to get a deep understanding of customer charges but also to inform purchasing content and micro-segmentation for advanced sales strategies. The shared view around an issue will stand beside spending models to determine next-to-buy models and deep marketing personalization.

The drawback to the broad selection of AI is a lack of training data. For large tech companies similar to GoogleApple and Amazon, collecting data is not a difficult task in comparison to most companies. Because of the range and depth of their products and services, they have a near-endless equipment of diverse data streams, forming the perfect background for their data scientists to train their algorithms. For smaller organizations, access to similar datasets is limited or simply too costly.

5. We will see this need for data satisfied by growing availability of artificial datasets

This provides less stressful or smaller companies to make meaningful gaits in their AI journey. Artificial data is data that is generated programmatically. For example, realistic pictures of objects in random scenes rendered using video game motors or audio formed by a speech unit model from the known text. The two most popular strategies for false data usage we will see are:

  • Taking notes from real statistic distributions and generating fake data according to these models;
  •  A model is designed to explain perceived behaviour, and then creates variable data using this type. It helps in the knowledge of the results of communications between different agents that are had on the way as a whole.

Companies who thought their data storage facilities to be minimal will understand that they need a modern solution to house their synthetic data if they are to play on the hard-hitting elements of machine learning.

It’s clear that data is an essential asset in the company, close to the center of every successful organization today. And, it looks like we’re in for extra boundary-pushing year in 2020, with data movement to open up endless opportunities, from a strategy and data science aspect. It’ll be exciting to see where we are this time next year for the 2021 forecasts.